Plaque expression levels of HDAC9 in association with plaque vulnerability traits and secondary vascular events in patients undergoing carotid endarterectomy: an analysis in the Athero-EXPRESS Biobank.
Athero-Express RNA sequencing preparation
Sander W. van der Laan, PhD | @swvanderlaan | s.w.vanderlaan@gmail.com
2022-03-18
General Setup
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[1] "/Users/swvanderlaan/git/CirculatoryHealth/AE_20211201_YAW_SWVANDERLAAN_HDAC9"
[1] "_archived" "1. AE_20211201_YAW_SWVANDERLAAN_HDAC9.nb.html" "1. AE_20211201_YAW_SWVANDERLAAN_HDAC9.Rmd"
[4] "2. SNP_analyses.Rmd" "20220317.HDAC9.AESCRNA.results.RData" "20220317.HDAC9.baseline.RData"
[7] "3.1 bulkRNAseq.preparation.nb.html" "3.1 bulkRNAseq.preparation.Rmd" "3.2 bulkRNAseq.main_analysis.nb.html"
[10] "3.2 bulkRNAseq.main_analysis.Rmd" "3.3 bulkRNAseq.additional_figures.Rmd" "3.4 bulkRNAseq.review_comments.Rmd"
[13] "4. report.scrnaseq.nb.html" "4. report.scrnaseq.Rmd" "AE_20211201_YAW_SWVANDERLAAN_HDAC9.Rproj"
[16] "AnalysisPlan" "HDAC9" "images"
[19] "LICENSE" "README.html" "README.md"
[22] "references.bib" "renv" "renv.lock"
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Today = format(as.Date(as.POSIXlt(Sys.time())), "%Y%m%d")
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### UtrechtScienceParkColoursScheme
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### ----------------------------------------------------------------------------ERA-CVD ‘druggable-MI-targets’
For the ERA-CVD ‘druggable-MI-targets’ project (grantnumber: 01KL1802) we performed two related RNA sequencing (RNAseq) experiments:
conventional (‘bulk’) RNAseq using RNA extracted from carotid plaque samples, n ± 700. As of Friday, March 18, 2022 all samples have been selected and RNA has been extracted; quality control (QC) was performed and we have a dataset of 635 samples.
single-cell RNAseq (scRNAseq) of at least n = 40 samples (20 females, 20 males). As of Friday, March 18, 2022 data is available of 40 samples (3 females, 15 males), we are extending sampling to get more female samples.
Plaque samples are derived from carotid endarterectomies as part of the Athero-Express Biobank Study which is an ongoing study in the UMC Utrecht.
Background
Here we obtain data from the HDAC9 in plaques.
library(openxlsx)
gene_list_df <- read.xlsx(paste0(PROJECT_loc, "/targets/Genes.xlsx"), sheet = "Genes")
target_genes <- unlist(gene_list_df$Gene)
target_genes[1] "HDAC9" "TWIST1" "IL6" "IL1B" Load data
First we will load the data:
- bulk RNA sequencing (RNAseq) experimental data from carotid plaques
- Athero-Express clinical data.
Bulk RNAseq data
Here we load the latest dataset from our Athero-Express bulk RNA experiment d.d. 2021-12-03 mapped to b37 and Ensembl 87.
These bulk RNAseq data are filtered and corrected:
- UMI corrected
- unmappable genes are excluded
# bulk RNAseq data
bulkRNA_counts_raw_qc_umicorr <- fread(paste0(AERNA_loc,"/raw_data_bulk/raw_counts_batch1till11_qc_umicorrected.txt"))
# batch information
bulkRNA_meta <- fread(paste0(AERNA_loc,"/raw_data_bulk/metadata_raw_counts_batch1till11.txt"))Quick peek at the counts and meta-data of the RNAseq experiment.
head(bulkRNA_counts_raw_qc_umicorr)
head(bulkRNA_meta)Annotating and fixing the RNAseq data
There are two small issues we need to address:
- annotation with chromosome, start/end, strand, and gene information
- fixing ±
Infvalues
Fixing infinite values
cat("\nThere are a couple of samples with infinite gene counts.\n")
There are a couple of samples with infinite gene counts.temp <- bulkRNA_counts_raw_qc_umicorr %>% mutate_if(is.numeric, as.integer)Warning: Problem with `mutate()` column `ae2341`.
ℹ `ae2341 = .Primitive("as.integer")(ae2341)`.
ℹ NAs introduced by coercion to integer range
Warning: Problem with `mutate()` column `ae3078`.
ℹ `ae3078 = .Primitive("as.integer")(ae3078)`.
ℹ NAs introduced by coercion to integer range
Warning: Problem with `mutate()` column `ae1422`.
ℹ `ae1422 = .Primitive("as.integer")(ae1422)`.
ℹ NAs introduced by coercion to integer range
Warning: Problem with `mutate()` column `ae2305`.
ℹ `ae2305 = .Primitive("as.integer")(ae2305)`.
ℹ NAs introduced by coercion to integer range
Warning: Problem with `mutate()` column `ae1256`.
ℹ `ae1256 = .Primitive("as.integer")(ae1256)`.
ℹ NAs introduced by coercion to integer range
Warning: Problem with `mutate()` column `ae411`.
ℹ `ae411 = .Primitive("as.integer")(ae411)`.
ℹ NAs introduced by coercion to integer range
Warning: Problem with `mutate()` column `ae1227`.
ℹ `ae1227 = .Primitive("as.integer")(ae1227)`.
ℹ NAs introduced by coercion to integer rangesummary(bulkRNA_counts_raw_qc_umicorr$ae2341) Min. 1st Qu. Median Mean 3rd Qu. Max.
0 0 0 Inf 15 Inf summary(bulkRNA_counts_raw_qc_umicorr$ae3078) Min. 1st Qu. Median Mean 3rd Qu. Max.
0 0 1 Inf 20 Inf summary(bulkRNA_counts_raw_qc_umicorr$ae1422) Min. 1st Qu. Median Mean 3rd Qu. Max.
0 0 1 Inf 12 Inf summary(bulkRNA_counts_raw_qc_umicorr$ae2305) Min. 1st Qu. Median Mean 3rd Qu. Max.
0 0 0 Inf 7 Inf summary(bulkRNA_counts_raw_qc_umicorr$ae1256) Min. 1st Qu. Median Mean 3rd Qu. Max.
0 0 0 Inf 10 Inf summary(bulkRNA_counts_raw_qc_umicorr$ae411) Min. 1st Qu. Median Mean 3rd Qu. Max.
0 0 0 Inf 15 Inf summary(bulkRNA_counts_raw_qc_umicorr$ae1227) Min. 1st Qu. Median Mean 3rd Qu. Max.
0 0 1 Inf 11 Inf cat("\nFixing the infinite gene counts.\n")
Fixing the infinite gene counts.temp <- bulkRNA_counts_raw_qc_umicorr %>%
mutate(across( # For every column you want...
starts_with("ae"), # ...change all studynumber
~ case_when(
. == Inf ~ max(.[is.finite(.)]), # +Inf becomes the finite max.
. == -Inf ~ min(.[is.finite(.)]), # -Inf becomes the finite min.
TRUE ~ . # Other values stay the same.
)
)
)Annotating
library("devtools")
devtools::install_github("stephenturner/annotables")Using github PAT from envvar GITHUB_PAT
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* installing *source* package ‘annotables’ ...
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* DONE (annotables)library(dplyr)
library(annotables)
# Columns of interest
# entrez
# symbol
# chr
# start
# end
# strand
# biotype
# description
cat("\nChecking existence of duplicate ENSEMBL IDs - there shouldn't be any.\n")
Checking existence of duplicate ENSEMBL IDs - there shouldn't be any.id <- temp$ENSEMBL_gene_ID
id[ id %in% id[duplicated(id)] ]character(0)rm(id)cat("\nAnnotating with b37.\n")
Annotating with b37.bulkRNA_counts <- temp %>%
# arrange(p.adjusted) %>%
# head(20) %>%
inner_join(grch37, by=c("ENSEMBL_gene_ID"="ensgene")) %>%
# select(gene, estimate, p.adjusted, symbol, description) %>%
relocate(entrez, symbol, chr, start, end, strand, biotype, description,
.before = ae1618) %>%
dplyr::filter(duplicated(ENSEMBL_gene_ID) == FALSE)
head(bulkRNA_counts)
id <- bulkRNA_counts$ENSEMBL_gene_ID
id[ id %in% id[duplicated(id)] ]character(0)Clinical data
Loading Athero-Express clinical data that we previously saved in an RDS file.
AEDB.CEA <- readRDS(file = paste0(OUT_loc, "/20220317.HDAC9.AEDB.CEA.RDS"))Fix STUDY_NUMBER
We will fix the STUDY_NUMBER to match the bulkRNAseq
data.
AEDB.CEA$STUDY_NUMBER <- paste0("ae", AEDB.CEA$STUDY_NUMBER)
head(AEDB.CEA$STUDY_NUMBER)[1] "ae1" "ae2" "ae3" "ae4" "ae5" "ae6"AERNA
Tidy data
We have collected the clinical data, Athero-Express Biobank Study
AEDB and, the UMI-corrected, filtered bulk RNAseq data,
bulkRNA_counts and its meta-data,
bulkRNA-meta.
Here we will clean up the data and create a
SummarizedExperiment() object for downstream analyses anad
visualizations.
AEDB.CEA.sampleList <- AEDB.CEA$STUDY_NUMBER
# first 9 columns
# ENSEMBL_gene_ID
# entrez
# symbol
# chr
# start
# end
# strand
# biotype
# description
# match up with meta data of RNAseq experiment
bulkRNA_countsFilt <- bulkRNA_counts %>%
drop_na(chr) %>% # remove rows that have no information of start, end, chromosome and/or strand
dplyr::select(1:9, one_of(sort(as.character(AEDB.CEA.sampleList)))) # select gene expression of only patients in RNA-seq AE df, sort in same order as metadata study_number
dim(bulkRNA_countsFilt)[1] 59851 620study_samples_bulkNEW <- colnames(bulkRNA_counts[, -(1:9)])
length(study_samples_bulkNEW)[1] 656study_samples_AEDBCEA <- c(AEDB.CEA$STUDY_NUMBER)
setdif_samples_NEWvsAEDBCEA <- setdiff(study_samples_bulkNEW, study_samples_AEDBCEA)
setdif_samples_AEDBCEAvsNEW <- setdiff(study_samples_AEDBCEA, study_samples_bulkNEW)
AEDB_filt <- AEDB.CEA[AEDB.CEA$STUDY_NUMBER %in% setdif_samples_NEWvsAEDBCEA,]
table(AEDB_filt$Artery_summary, AEDB_filt$Gender)
female male
No artery known (yet), no surgery (patient ill, died, exited study), re-numbered to AAA 0 0
carotid (left & right) 0 0
femoral/iliac (left, right or both sides) 0 0
other carotid arteries (common, external) 0 0
carotid bypass and injury (left, right or both sides) 0 0
aneurysmata (carotid & femoral) 0 0
aorta 0 0
other arteries (renal, popliteal, vertebral) 0 0
femoral bypass, angioseal and injury (left, right or both sides) 0 0# Cut up bulkRNA_countsFilt into 'assay' and 'ranges' part
counts <- as.data.frame(bulkRNA_countsFilt[,-(1:9)]) ## assay part
counts <- counts %>% mutate_if(is.numeric, as.integer)
rownames(counts) <- bulkRNA_countsFilt$ENSEMBL_gene_ID ## assign rownames
id <- bulkRNA_countsFilt$ENSEMBL_gene_ID
id[ id %in% id[duplicated(id)] ]character(0)bulkRNA_rowRanges <- GRanges(bulkRNA_countsFilt$chr, ## construct a GRanges object containing 4 columns (seqnames, ranges, strand, seqinfo) plus a metadata colum (feature_id): this will be the 'rowRanges' bit
IRanges(bulkRNA_countsFilt$start, bulkRNA_countsFilt$end),
strand = bulkRNA_countsFilt$strand,
feature_id = bulkRNA_countsFilt$ENSEMBL_gene_ID) #, df$pid)
names(bulkRNA_rowRanges) <- bulkRNA_rowRanges$feature_id
# ?org.Hs.eg.db
# ?AnnotationDb
bulkRNA_rowRanges$symbol <- mapIds(org.Hs.eg.db,
keys = bulkRNA_rowRanges$feature_id,
column = "SYMBOL",
keytype = "ENSEMBL",
multiVals = "first")'select()' returned 1:many mapping between keys and columns# Reference: https://shiring.github.io/genome/2016/10/23/AnnotationDbi
# gene dataframe for EnsDb.Hsapiens.v86
gene_dataframe_EnsDb <- ensembldb::select(EnsDb.Hsapiens.v86, keys = bulkRNA_rowRanges$feature_id,
columns = c("ENTREZID", "SYMBOL", "GENEBIOTYPE"), keytype = "GENEID")
colnames(gene_dataframe_EnsDb) <- c("Ensembl", "Entrez", "HGNC", "GENEBIOTYPE")
colnames(gene_dataframe_EnsDb) <- paste(colnames(gene_dataframe_EnsDb), "EnsDb86", sep = "_")
head(gene_dataframe_EnsDb)
bulkRNA_rowRanges$GENEBIOTYPE_EnsDb86 <- gene_dataframe_EnsDb$GENEBIOTYPE_EnsDb86[match(bulkRNA_rowRanges$feature_id, gene_dataframe_EnsDb$Ensembl_EnsDb86)]
bulkRNA_rowRangesGRanges object with 59851 ranges and 3 metadata columns:
seqnames ranges strand | feature_id symbol GENEBIOTYPE_EnsDb86
<Rle> <IRanges> <Rle> | <character> <character> <character>
ENSG00000000003 X 100627108-100639991 - | ENSG00000000003 TSPAN6 protein_coding
ENSG00000000419 20 50934867-50959140 - | ENSG00000000419 DPM1 protein_coding
ENSG00000000457 1 169849631-169894267 - | ENSG00000000457 SCYL3 protein_coding
ENSG00000000460 1 169662007-169854080 + | ENSG00000000460 C1orf112 protein_coding
ENSG00000000938 1 27612064-27635185 - | ENSG00000000938 FGR protein_coding
... ... ... ... . ... ... ...
ENSG00000248205 5 17502043-17502363 - | ENSG00000248205 <NA> processed_pseudogene
ENSG00000259098 15 22258138-22258848 + | ENSG00000259098 <NA> processed_pseudogene
ENSG00000267090 19 38385522-38386759 + | ENSG00000267090 <NA> antisense
ENSG00000279119 17 38727833-38728198 - | ENSG00000279119 <NA> protein_coding
ENSG00000271242 13 51195881-51196081 + | ENSG00000271242 PRELID3BP2 processed_pseudogene
-------
seqinfo: 338 sequences from an unspecified genome; no seqlengths# merging the two dataframes by HGNC
# bulkRNA_rowRangesHg19Ensemblb86 <- GRanges(merge(bulkRNA_rowRanges, gene_dataframe_EnsDb, by.x = "feature_id", by.y = "Ensembl_EnsDb86", sort = FALSE, all.x = TRUE))
# names(bulkRNA_rowRangesHg19Ensemblb86) <- bulkRNA_rowRangesHg19Ensemblb86$feature_id
# bulkRNA_rowRangesHg19Ensemblb86
# temp <- as.data.frame(table(bulkRNA_rowRanges$GENEBIOTYPE_EnsDb86))
# colnames(temp) <- c("GeneBiotype", "Count")
#
# ggpubr::ggbarplot(temp, x = "GeneBiotype", y = "Count",
# color = "GeneBiotype", fill = "GeneBiotype",
# xlab = "gene type") +
# theme(axis.text.x = element_text(angle = 45))
# rm(temp)# match up with meta data of RNAseq experiment
bulkRNA_meta %<>%
dplyr::filter(study_number %in% AEDB.CEA.sampleList) # select gene expression of only patients in RNA-seq AE df, sort in same order as metadata study_number
# combine meta data from experiment with clinical data
bulkRNA_meta_clin <- merge(bulkRNA_meta, AEDB.CEA, by.x = "study_number", by.y = "STUDY_NUMBER",
sort = FALSE, all.x = TRUE)
bulkRNA_meta_clin %<>%
# mutate(macrophages = factor(macrophages, levels = c("no staining", "minor staining", "moderate staining", "heavy staining"))) %>%
# mutate(smc = factor(smc, levels = c("no staining", "minor staining", "moderate staining", "heavy staining"))) %>%
# mutate(calcification = factor(calcification, levels = c("no staining", "minor staining", "moderate staining", "heavy staining"))) %>%
# mutate(collagen = factor(collagen, levels = c("no staining", "minor staining", "moderate staining", "heavy staining"))) %>%
# mutate(fat = factor(fat, levels = c("no fat", "< 40% fat", "> 40% fat"))) %>%
mutate(study_number_row = study_number) %>%
as.data.frame() %>%
column_to_rownames("study_number_row")
head(bulkRNA_meta_clin)dim(bulkRNA_meta_clin)[1] 650 1714SummarizedExperiment
We make a SummarizedExperiment for the RNAseq data. We
make sure to only include the samples we need based on informed consent
and we include only the requested variables.
First, we define the variables we need.
# Baseline table variables
basetable_vars = c("Hospital", "ORyear", "Artery_summary",
"Age", "Gender",
# "TC_finalCU", "LDL_finalCU", "HDL_finalCU", "TG_finalCU",
"TC_final", "LDL_final", "HDL_final", "TG_final",
# "hsCRP_plasma",
"systolic", "diastoli", "GFR_MDRD", "BMI",
"KDOQI", "BMI_WHO",
"SmokerStatus", "AlcoholUse",
"DiabetesStatus",
"Hypertension.selfreport", "Hypertension.selfreportdrug", "Hypertension.composite", "Hypertension.drugs",
"Med.anticoagulants", "Med.all.antiplatelet", "Med.Statin.LLD",
"Stroke_Dx", "sympt", "Symptoms.5G", "AsymptSympt", "AsymptSympt2G",
"Symptoms.Update2G", "Symptoms.Update3G",
"restenos", "stenose",
"CAD_history", "PAOD", "Peripheral.interv",
"EP_composite", "EP_composite_time", "EP_major", "EP_major_time",
"MAC_rankNorm", "SMC_rankNorm", "Macrophages.bin", "SMC.bin",
"Neutrophils_rankNorm", "MastCells_rankNorm",
"IPH.bin", "VesselDensity_rankNorm",
"Calc.bin", "Collagen.bin",
"Fat.bin_10", "Fat.bin_40",
"OverallPlaquePhenotype", "Plaque_Vulnerability_Index")
basetable_bin = c("Gender", "Artery_summary",
"KDOQI", "BMI_WHO",
"SmokerStatus", "AlcoholUse",
"DiabetesStatus",
"Hypertension.selfreport", "Hypertension.selfreportdrug", "Hypertension.composite", "Hypertension.drugs",
"Med.anticoagulants", "Med.all.antiplatelet", "Med.Statin.LLD",
"Stroke_Dx", "sympt", "Symptoms.5G", "AsymptSympt", "AsymptSympt2G",
"Symptoms.Update2G", "Symptoms.Update3G",
"restenos", "stenose",
"CAD_history", "PAOD", "Peripheral.interv",
"EP_composite", "Macrophages.bin", "SMC.bin",
"IPH.bin",
"Calc.bin", "Collagen.bin",
"Fat.bin_10", "Fat.bin_40",
"OverallPlaquePhenotype", "Plaque_Vulnerability_Index")
# basetable_bin
basetable_con = basetable_vars[!basetable_vars %in% basetable_bin]
# basetable_conNext, we are constructing the SummarizedExperiment.
cat("* loading data ...\n")* loading data ...# this is all the data passing RNAseq quality control and UMI-corrected
# - includes 656 patients
# - after filtering on informed consent and artery type, the end sample size should be 611
# - after filtering on 'no commercial business' based on informed consent, there are fewer samples: 608
dim(bulkRNA_countsFilt)[1] 59851 620dim(counts)[1] 59851 611cat("\n* making a SummarizedExperiment ...\n")
* making a SummarizedExperiment ...cat(" > getting counts\n") > getting countshead(counts)head(bulkRNA_countsFilt)
cat(" > meta data\n") > meta datatemp_coldat <- data.frame(STUDY_NUMBER = names(bulkRNA_countsFilt[,10:620]),
SampleType = "plaque", RNAseqType = "3' RNAseq",
row.names = names(bulkRNA_countsFilt[,10:620]))
cat(" > clinical data\n") > clinical data# bulkRNA_meta_clin_COMMERCIAL <- subset(bulkRNA_meta_clin, select = c("study_number", basetable_vars))
bulkRNA_meta_clin_ACADEMIC <- subset(bulkRNA_meta_clin, select = c("study_number", basetable_vars))
# temp_coldat_clin <- merge(temp_coldat, bulkRNA_meta_clin_COMMERCIAL, by.x = "STUDY_NUMBER", by.y = "study_number", sort = FALSE, all.x = TRUE)
temp_coldat_clin <- merge(temp_coldat, bulkRNA_meta_clin_ACADEMIC, by.x = "STUDY_NUMBER", by.y = "study_number", sort = FALSE, all.x = TRUE)
rownames(temp_coldat_clin) <- temp_coldat_clin$STUDY_NUMBER
dim(temp_coldat_clin)[1] 611 58cat(" > construction of the SE\n") > construction of the SE(AERNASE <- SummarizedExperiment(assays = list(counts = as.matrix(counts)),
colData = temp_coldat_clin,
rowRanges = bulkRNA_rowRanges,
metadata = "Athero-Express Biobank Study bulk RNA sequencing. Sample type: carotid plaques. Technology: CEL2-seq adapted for bulk RNA sequencing, thus 3'-focused. UMI-corrected"))class: RangedSummarizedExperiment
dim: 59851 611
metadata(1): ''
assays(1): counts
rownames(59851): ENSG00000000003 ENSG00000000419 ... ENSG00000279119 ENSG00000271242
rowData names(3): feature_id symbol GENEBIOTYPE_EnsDb86
colnames(611): ae1 ae1026 ... ae998 ae999
colData names(58): STUDY_NUMBER SampleType ... OverallPlaquePhenotype Plaque_Vulnerability_Indexcat("\n* removing intermediate files ...\n")
* removing intermediate files ...rm(temp_coldat, temp_coldat_clin)Do the study numbers correspond between metadata and expression data?
## check whether rownames metadata and colnames counts are identical
all(colnames(AERNASE) == colnames(counts))[1] TRUESo, now we have raw counts for all patients included in the bulk RNAseq data, with all clinical data annotated to them.
Some of the patients might be missing in certain variables:
# We know that some of the patients of the RNAseq is not included in some variables
which(is.na(AERNASE$Gender))
missing_values <- which(is.na(AERNASE$Gender))
missing_valuesNo need to remove missing samples based on a variable, since we will make a DESeq2 object using an empty model.
(AERNASE <- AERNASE[,])
# (AERNASE <- AERNASE[, -missing_values])
# (se <- se[, se$sex == "male"])Baseline
Preparation
cat("====================================================================================================")====================================================================================================cat("SELECTION THE SHIZZLE")SELECTION THE SHIZZLEAERNASEClinData <- as.tibble(colData(AERNASE))Warning: `as.tibble()` was deprecated in tibble 2.0.0.
Please use `as_tibble()` instead.
The signature and semantics have changed, see `?as_tibble`.
This warning is displayed once every 8 hours.
Call `lifecycle::last_lifecycle_warnings()` to see where this warning was generated.cat("- sanity checking PRIOR to selection")- sanity checking PRIOR to selectionlibrary(data.table)
require(labelled)Loading required package: labelledae.gender <- to_factor(AERNASEClinData$Gender)
ae.hospital <- to_factor(AERNASEClinData$Hospital)
table(ae.gender, ae.hospital, dnn = c("Sex", "Hospital"), useNA = "ifany") Hospital
Sex St. Antonius, Nieuwegein UMC Utrecht
female 94 54
male 255 208ae.artery <- to_factor(AERNASEClinData$Artery_summary)
table(ae.artery, ae.gender, dnn = c("Sex", "Artery"), useNA = "ifany") Artery
Sex female male
No artery known (yet), no surgery (patient ill, died, exited study), re-numbered to AAA 0 0
carotid (left & right) 148 461
femoral/iliac (left, right or both sides) 0 0
other carotid arteries (common, external) 0 2
carotid bypass and injury (left, right or both sides) 0 0
aneurysmata (carotid & femoral) 0 0
aorta 0 0
other arteries (renal, popliteal, vertebral) 0 0
femoral bypass, angioseal and injury (left, right or both sides) 0 0rm(ae.gender, ae.hospital, ae.artery)
# AERNASEClinData[1:10, 1:10]
dim(AERNASEClinData)[1] 611 58# DT::datatable(AERNASEClinData)Showing the baseline table for the scRNAseq data in 39 CEA patients with informed consent.
cat("===========================================================================================")===========================================================================================cat("CREATE BASELINE TABLE")CREATE BASELINE TABLE# Create baseline tables
# http://rstudio-pubs-static.s3.amazonaws.com/13321_da314633db924dc78986a850813a50d5.html
AERNASEClinData.CEA.tableOne = print(CreateTableOne(vars = basetable_vars,
# factorVars = basetable_bin,
# strata = "Gender",
data = AERNASEClinData, includeNA = TRUE),
nonnormal = c(),
quote = FALSE, showAllLevels = TRUE,
format = "p",
contDigits = 3)[,1:2]
level Overall
n 611
Hospital (%) St. Antonius, Nieuwegein 57.1
UMC Utrecht 42.9
ORyear (%) 2002 5.2
2003 10.0
2004 10.6
2005 13.1
2006 13.3
2007 10.5
2008 10.1
2009 11.0
2010 5.6
2011 5.1
2012 3.6
2013 0.8
2014 0.5
2015 0.5
2016 0.2
2017 0.0
2018 0.0
2019 0.0
2020 0.0
2021 0.0
2022 0.0
Artery_summary (%) No artery known (yet), no surgery (patient ill, died, exited study), re-numbered to AAA 0.0
carotid (left & right) 99.7
femoral/iliac (left, right or both sides) 0.0
other carotid arteries (common, external) 0.3
carotid bypass and injury (left, right or both sides) 0.0
aneurysmata (carotid & femoral) 0.0
aorta 0.0
other arteries (renal, popliteal, vertebral) 0.0
femoral bypass, angioseal and injury (left, right or both sides) 0.0
Age (mean (SD)) 34.484 (8.897)
Gender (%) female 24.2
male 75.8
TC_final (mean (SD)) 4.663 (1.259)
LDL_final (mean (SD)) 2.774 (1.046)
HDL_final (mean (SD)) 1.147 (0.375)
TG_final (mean (SD)) 1.605 (0.942)
systolic (mean (SD)) 65.356 (24.793)
diastoli (mean (SD)) 43.364 (13.409)
GFR_MDRD (mean (SD)) 1326.275 (661.445)
BMI (mean (SD)) 667.110 (299.088)
KDOQI (%) Normal kidney function 18.2
CKD 2 (Mild) 55.8
CKD 3 (Moderate) 23.2
CKD 4 (Severe) 1.5
CKD 5 (Failure) 0.0
<NA> 1.3
BMI_WHO (%) Underweight 0.8
Normal 33.2
Overweight 46.2
Obese 14.7
<NA> 5.1
SmokerStatus (%) Current smoker 36.0
Ex-smoker 44.0
Never smoked 16.2
<NA> 3.8
AlcoholUse (%) Yes 34.0
<NA> 66.0
DiabetesStatus (%) Diabetes 78.4
<NA> 21.6
Hypertension.selfreport (%) no 27.3
yes 70.5
<NA> 2.1
Hypertension.selfreportdrug (%) no 33.7
yes 63.8
<NA> 2.5
Hypertension.composite (%) no 13.3
yes 86.7
Hypertension.drugs (%) no 22.9
yes 76.9
<NA> 0.2
Med.anticoagulants (%) no 87.7
yes 12.1
<NA> 0.2
Med.all.antiplatelet (%) no 10.5
yes 89.4
<NA> 0.2
Med.Statin.LLD (%) no 24.5
yes 75.3
<NA> 0.2
Stroke_Dx (%) No stroke diagnosed 75.5
Stroke diagnosed 18.0
<NA> 6.5
sympt (%) Asymptomatic 13.1
TIA 41.2
minor stroke 15.4
Major stroke 9.3
Amaurosis fugax 15.9
Four vessel disease 0.0
Vertebrobasilary TIA 0.2
Retinal infarction 1.5
Symptomatic, but aspecific symtoms 2.6
Contralateral symptomatic occlusion 0.5
retinal infarction 0.2
armclaudication due to occlusion subclavian artery, CEA needed for bypass 0.0
retinal infarction + TIAs 0.0
Ocular ischemic syndrome 0.2
ischemisch glaucoom 0.0
subclavian steal syndrome 0.0
TGA 0.0
Symptoms.5G (%) Ocular 9.5
Other 19.0
Retinal infarction 1.6
Stroke 56.6
TIA 13.3
AsymptSympt (%) Ocular and others 30.1
Symptomatic 69.9
AsymptSympt2G (%) Symptomatic 100.0
Symptoms.Update2G (%) Symptomatic 75.5
<NA> 24.5
Symptoms.Update3G (%) Symptomatic 75.5
<NA> 24.5
restenos (%) de novo 95.9
restenosis 1.8
stenose bij angioseal na PTCA 0.0
<NA> 2.3
stenose (%) 0-49% 0.3
50-70% 6.2
70-90% 44.0
90-99% 44.8
100% (Occlusion) 0.8
NA 0.0
50-99% 0.2
70-99% 0.0
99 0.0
<NA> 3.6
CAD_history (%) No history CAD 66.6
History CAD 33.4
PAOD (%) no 79.5
yes 20.5
Peripheral.interv (%) no 84.8
yes 15.2
EP_composite (%) No composite endpoints 75.0
Composite endpoints 24.5
<NA> 0.5
EP_composite_time (mean (SD)) 2.655 (1.143)
EP_major (%) No major events (endpoints) 86.3
Major events (endpoints) 13.3
<NA> 0.5
EP_major_time (mean (SD)) 2.845 (1.021)
MAC_rankNorm (mean (SD)) 0.276 (0.961)
SMC_rankNorm (mean (SD)) -0.041 (0.930)
Macrophages.bin (%) no/minor 42.7
moderate/heavy 55.5
<NA> 1.8
SMC.bin (%) no/minor 31.6
moderate/heavy 66.6
<NA> 1.8
Neutrophils_rankNorm (mean (SD)) 0.256 (1.020)
MastCells_rankNorm (mean (SD)) -0.021 (1.025)
IPH.bin (%) no 37.6
yes 60.9
<NA> 1.5
VesselDensity_rankNorm (mean (SD)) 0.139 (0.948)
Calc.bin (%) no/minor 46.3
moderate/heavy 52.4
<NA> 1.3
Collagen.bin (%) no/minor 19.1
moderate/heavy 79.4
<NA> 1.5
Fat.bin_10 (%) <10% 23.4
>10% 75.3
<NA> 1.3
Fat.bin_40 (%) <40% 68.9
>40% 29.8
<NA> 1.3
OverallPlaquePhenotype (%) atheromatous 37.3
fibroatheromatous 31.3
fibrous 0.0
<NA> 31.4
Plaque_Vulnerability_Index (%) 0 7.4
1 16.5
2 25.5
3 33.1
4 11.9
5 5.6 Writing the baseline table to Excel format.
# Write basetable
require(openxlsx)
# write.xlsx(file = paste0(BASELINE_loc, "/",Today,".",PROJECTNAME,".AERNA.CEA.608pts.after_qc.IC_commercial.BaselineTable..xlsx"),
# format(AERNASEClinData.CEA.tableOne, digits = 5, scientific = FALSE) ,
# rowNames = TRUE, colNames = TRUE, overwrite = TRUE)
#
write.xlsx(file = paste0(BASELINE_loc, "/",Today,".",PROJECTNAME,".AERNA.CEA.611pts.after_qc.IC_academic.BaselineTable..xlsx"),
format(AERNASEClinData.CEA.tableOne, digits = 5, scientific = FALSE) ,
rowNames = TRUE, colNames = TRUE, overwrite = TRUE)Expression differences
From here we can analyze whether specific genes differ between groups, or do this for the entire gene set as part of DE analysis, and then select our genes of interest. Let’s start with the former.
Prepare DDS and VSD
The dds raw counts need normalization and log transformation first.
AERNAdds <- DESeqDataSet(AERNASE, design = ~ 1)
# Determine the size factors to use for normalization
AERNAdds <- estimateSizeFactors(AERNAdds)
# sizeFactors(AERNAdds)
# Extract the normalized counts
normalized_counts <- counts(AERNAdds, normalized = TRUE)
# head(normalized_counts)
# Log transform counts for QC
AERNAvsd <- vst(AERNAdds, blind = TRUE)
# There is a message stating the following.
#
# -- note: fitType='parametric', but the dispersion trend was not well captured by the
# function: y = a/x + b, and a local regression fit was automatically substituted.
# specify fitType='local' or 'mean' to avoid this message next time.
#
# No action is required.
#
# For more information check: https://www.biostars.org/p/119115/Extract data of interest
From here, extract the gene expression values, plus gene identifier, annotate with gene symbol, and select the genes of our interest.
expression_data <- assay(AERNAvsd)
# extract expression values from vsd, including ensembl names
expression_data <- as_tibble(data.frame(gene_ensembl = rowRanges(AERNAvsd)$feature_id, assay(AERNAvsd))) %>%
mutate_at(vars(c("gene_ensembl")), list(as.character)) ## gene_ensembl needs to be character for annotation to work
# annotations
# gene symbol - via org.Hs.eg.db
# columns(org.Hs.eg.db)
expression_data$symbol <- mapIds(org.Hs.eg.db,
keys = expression_data$gene_ensembl,
column = "SYMBOL",
keytype = "ENSEMBL",
multiVals = "first")'select()' returned 1:many mapping between keys and columns# tidy and subset
expression_data_sel <- expression_data %>%
dplyr::select(gene_ensembl, symbol, everything()) %>%
# filter(symbol == "APOE" | symbol == "TRIB3") %>% # filter APOE and TRIB3
dplyr::filter(symbol %in% target_genes)
head(expression_data_sel)
# tidy and subset non-selected genes
set.seed(141619)
expression_data_sample <- expression_data %>%
dplyr::select(gene_ensembl, symbol, everything()) %>%
sample_n(1000) %>%
unite(symbol_ensembl, symbol, gene_ensembl, sep = "_", remove = FALSE)
expression_data_sample_mean <- expression_data_sample %>%
select_if(is.numeric) %>%
colMeans() %>%
as_tibble(rownames = "study_number") %>%
dplyr::rename(expression_value_sample = value)Furthermore, the expression_data_sel df was gathered into a long form
df for annotation with symptoms variables from the
vsd object, and later visualization and statistics.
# gather expression_data_sel df into long df form for annotation, plotting and statistics
expression_long <-
gather(expression_data_sel, key = "study_number", value = "expression_value", -c(gene_ensembl, symbol))
# old school way
# Annotate with smoking variables
# sample_ids <- expression_long$study_number
# mm <- match(expression_long$study_number, rownames(colData(vsd)))
#
# ## Add traits to df
# ## Binary traits
# expression_long$sex <- colData(vsd)$sex[mm]
# expression_long$testosterone <- colData(vsd)$testosterone[mm]
# expression_long$t_e2_ratio <- colData(vsd)$t_e2_ratio[mm]
# new school way
plaque_phenotypes_cat <- c("Macrophages.bin",
"SMC.bin",
"Calc.bin",
"Collagen.bin",
"Fat.bin_10",
# "Fat.bin_40",
"IPH.bin")
plaque_phenotypes_num <- c("MAC_rankNorm", #"macmean0",
"SMC_rankNorm", #"smcmean0",
# "MastCells_rankNorm", #"mast_cells_plaque",
# "Neutrophils_rankNorm", #"neutrophils",
"VesselDensity_rankNorm") #"vessel_density")
expression_long <- expression_long %>%
left_join(bulkRNA_meta_clin %>% dplyr::select(study_number,
plaque_phenotypes_cat,
plaque_phenotypes_num,
epmajor.3years, epmajor.30days,
AsymptSympt2G,
Gender, Hospital),
by = "study_number") %>%
mutate(epmajor_3years_yn = str_replace_all(epmajor.3years, c("Excluded" = "yes", "Included" = "no"))) %>%
mutate(epmajor.30days_yn = str_replace_all(epmajor.30days, c("Excluded" = "yes", "Included" = "no")))Note: Using an external vector in selections is ambiguous.
ℹ Use `all_of(plaque_phenotypes_cat)` instead of `plaque_phenotypes_cat` to silence this message.
ℹ See <https://tidyselect.r-lib.org/reference/faq-external-vector.html>.
This message is displayed once per session.
Note: Using an external vector in selections is ambiguous.
ℹ Use `all_of(plaque_phenotypes_num)` instead of `plaque_phenotypes_num` to silence this message.
ℹ See <https://tidyselect.r-lib.org/reference/faq-external-vector.html>.
This message is displayed once per session.head(expression_long)
# expression_long %>%
# write_tsv("genes_interest_expression.txt")Gene expression - distribution
Filter genes
In case some genes are not available in our data we could filter them here.
target_genes[1] "HDAC9" "TWIST1" "IL6" "IL1B" This code is just an example to filter the list from genes that are not in the data.
# target_genes_rm <- c("AC011294.3", "C6orf195", "C9orf53", "AL137026.1", "DUPD1", "RP11-145E5.5", "PVRL2",
# "LINC00841", "LOC100130539")
#
# temp = target_genes[!target_genes %in% target_genes_rm]
#
# target_genes_qc <- c(temp)
target_genes_qc <- target_genes
# for debug
# target_genes_qc_replace <- c("LINC01600", "DUSP27", "NECTIN2", "C10orf142", "LINC02881")Plotting expression
Figure 1: Expression of genes of interest: boxplots
# Make directory for plots
ifelse(!dir.exists(file.path(QC_loc, "/Boxplots")),
dir.create(file.path(QC_loc, "/Boxplots")),
FALSE)[1] FALSEBOX_loc = paste0(QC_loc,"/Boxplots")
for(GENE in target_genes_qc){
cat(paste0("Plotting expression for ", GENE,".\n"))
temp <- subset(expression_long, symbol == GENE)
compare_means(expression_value ~ Gender, data = temp)
p1 <- ggpubr::ggboxplot(temp,
x = "Gender",
y = "expression_value",
color = "Gender",
palette = "npg",
add = "jitter",
ylab = paste0("normalized expression ", GENE,"" ),
repel = TRUE
) + stat_compare_means()
#print(p1)
cat(paste0("Saving image for ", GENE,".\n"))
ggsave(filename = paste0(BOX_loc, "/", Today, ".",GENE,".expression_vs_gender.png"), plot = last_plot())
ggsave(filename = paste0(BOX_loc, "/", Today, ".",GENE,".expression_vs_gender.pdf"), plot = last_plot())
rm(temp, p1 )
}Plotting expression for HDAC9.
Saving image for HDAC9.Saving 7 x 7 in image
Saving 7 x 7 in imagePlotting expression for TWIST1.
Saving image for TWIST1.Saving 7 x 7 in image
Saving 7 x 7 in imagePlotting expression for IL6.
Saving image for IL6.Saving 7 x 7 in image
Saving 7 x 7 in imagePlotting expression for IL1B.
Saving image for IL1B.Saving 7 x 7 in image
Saving 7 x 7 in imageFigure 2A: Expression of genes of interest: histograms
# Make directory for plots
ifelse(!dir.exists(file.path(QC_loc, "/Histograms")),
dir.create(file.path(QC_loc, "/Histograms")),
FALSE)[1] FALSEHISTOGRAM_loc = paste0(QC_loc,"/Histograms")
for(GENE in target_genes_qc){
# cat(paste0("Plotting expression for ", GENE,".\n"))
temp <- subset(expression_long, symbol == GENE)
p1 <- ggpubr::gghistogram(temp,
x = "expression_value",
y = "..count..",
color = "Gender", fill = "Gender",
palette = "npg",
add = "median",
ylab = paste0("normalized expression ", GENE,"" )
)
# print(p1)
cat(paste0("Saving image for ", GENE,".\n"))
ggsave(filename = paste0(HISTOGRAM_loc, "/", Today, ".",GENE,".distribution.png"), plot = last_plot())
# ggsave(filename = paste0(HISTOGRAM_loc, "/", Today, ".",GENE,".distribution.pdf"), plot = last_plot())
rm(temp, p1 )
}Saving image for HDAC9.Saving 7 x 7 in imageSaving image for TWIST1.Saving 7 x 7 in imageSaving image for IL6.Saving 7 x 7 in imageSaving image for IL1B.Saving 7 x 7 in imageFigure 2B: Expression of genes of interest: density plots
# Make directory for plots
ifelse(!dir.exists(file.path(QC_loc, "/Density")),
dir.create(file.path(QC_loc, "/Density")),
FALSE)[1] FALSEDENSITY_loc = paste0(QC_loc,"/Density")
for(GENE in target_genes_qc){
# cat(paste0("Plotting expression for ", GENE,".\n"))
temp <- subset(expression_long, symbol == GENE)
p1 <- ggpubr::gghistogram(temp,
x = "expression_value",
y = "..density..",
color = "Gender", fill = "Gender",
palette = "npg",
add = "median",
ylab = paste0("normalized expression ", GENE,"" )
)
# print(p1)
cat(paste0("Saving image for ", GENE,".\n"))
ggsave(filename = paste0(DENSITY_loc, "/", Today, ".",GENE,".density.png"), plot = last_plot())
# ggsave(filename = paste0(DENSITY_loc, "/", Today, ".",GENE,".density.pdf"), plot = last_plot())
rm(temp, p1 )
}Saving image for HDAC9.Saving 7 x 7 in imageSaving image for TWIST1.Saving 7 x 7 in imageSaving image for IL6.Saving 7 x 7 in imageSaving image for IL1B.Saving 7 x 7 in imageCompare expression to the expression of a sample of 1,000 genes
Figure 3: comparing expression of genes of interest to mean expression of a sample of 1,000 random genes
expression_wide <- expression_long %>%
dplyr::select(-gene_ensembl) %>%
spread(key = symbol, value = expression_value)# the next 3 lines of code gave an error when selecting for genes_interest, since one of the genes of interest is missing: FGF3 is not in the data set. So, we need to select for the other 15 genes.
# genes_interest <- genes_interest[genes_interest$Symbol %in% unique(expression_long$symbol),]
# target_genes_qc
expression_wide2 <- expression_wide %>%
left_join(expression_data_sample_mean, by = "study_number") %>%
dplyr::select(study_number, target_genes_qc, expression_value_sample)Note: Using an external vector in selections is ambiguous.
ℹ Use `all_of(target_genes_qc)` instead of `target_genes_qc` to silence this message.
ℹ See <https://tidyselect.r-lib.org/reference/faq-external-vector.html>.
This message is displayed once per session.expression_long2 <- expression_wide2 %>%
gather(gene, expression_value, -study_number) %>%
mutate(gene = str_replace_all(gene, c("expression_value_sample" = "Random genes"))) #%>%
# mutate(gene = factor(gene, levels = c("Random genes", target_genes_qc)))
mean_1000_genes <- mean(expression_data_sample_mean$expression_value_sample)
# head(expression_long2)
#
p1 <- ggpubr::ggboxplot(expression_long2,
x = "gene",
y = "expression_value",
color = uithof_color[16],
add = "jitter",
add.params = list(size = 3, jitter = 0.3),
ylab = paste0("normalized expression ")
) +
geom_hline(yintercept = mean_1000_genes, linetype = "dashed", color = uithof_color[26], size = 1.25) +
theme(axis.text.x = element_text(size = 18, angle = 45, hjust = 1, vjust = 1), # change orientation of x-axis labels
axis.text.y = element_text(size = 18),
axis.title.x = element_text(size = 20),
axis.title.y = element_text(size = 20))
p1
ggsave(filename = paste0(PLOT_loc, "/", Today, ".TargetExpression_vs_1000genes.png"), plot = last_plot())Saving 18 x 12 in image ggsave(filename = paste0(PLOT_loc, "/", Today, ".TargetExpression_vs_1000genes.pdf"), plot = last_plot())Saving 18 x 12 in image
rm(p1 )Heatmaps for genes of interest
If we would put these correlations in one simple and comprehensible figure, we could use a correlation heatmap. Again, correlation coefficients used here are Spearman’s.
Figure 4: correlation heatmap between expression of genes of interest
library(tidyverse)
library(magrittr)
temp <- expression_wide %>%
column_to_rownames("study_number") %>%
dplyr::select(target_genes_qc) %>%
drop_na() %>% # drop NA
Filter(function(x) sd(x) != 0, .) # filter variables with sd = 0
temp.cor <- cor(temp, method = "spearman")
p1 <- pheatmap(data.matrix(temp.cor),
scale = "none",
cluster_rows = TRUE,
cluster_cols = TRUE,
legend = TRUE,
fontsize = 18)
p1
ggsave(filename = paste0(PLOT_loc, "/", Today, ".correlations.target_genes.pdf"), plot = p1, height = 15, width = 15)
ggsave(filename = paste0(PLOT_loc, "/", Today, ".correlations.target_genes.png"), plot = p1, height = 15, width = 15)
rm(temp, temp.cor, p1)Gene target list
We are saving the final list of genes of interest
temp <- subset(expression_data_sel, select = c("gene_ensembl", "symbol"))
fwrite(temp,
file = paste0(OUT_loc, "/", Today, ".target_list.qc.txt"),
sep = " ", row.names = FALSE, col.names = TRUE,
showProgress = TRUE)
rm(temp)Saving AERNA data
We will create a list of samples that should be included based on
CEA, and having the proper informed consent (‘academic’). We will save
the SummarizedExperiment as a RDS file for easy loading.
The clinical data will also be saved as a separate
txt-file.
temp <- as.tibble(subset(colData(AERNASE), select = c("STUDY_NUMBER", "SampleType", "RNAseqType")))
# fwrite(temp,
# file = paste0(OUT_loc, "/", Today, ".AERNA.CEA.608pts.samplelist.after_qc.IC_commercial.csv"),
# sep = ",", row.names = FALSE, col.names = TRUE,
# showProgress = TRUE)
# rm(temp)
#
# temp <- as.tibble(colData(AERNASE))
#
# fwrite(temp,
# file = paste0(OUT_loc, "/", Today, ".AERNA.CEA.608pts.clinicaldata.after_qc.IC_commercial.csv"),
# sep = ",", row.names = FALSE, col.names = TRUE,
# showProgress = TRUE)
# rm(temp)
fwrite(temp,
file = paste0(OUT_loc, "/", Today, ".AERNA.CEA.611pts.samplelist.after_qc.IC_academic.csv"),
sep = ",", row.names = FALSE, col.names = TRUE,
showProgress = TRUE)
rm(temp)
temp <- as.tibble(colData(AERNASE))
fwrite(temp,
file = paste0(OUT_loc, "/", Today, ".AERNA.CEA.611pts.clinicaldata.after_qc.IC_academic.csv"),
sep = ",", row.names = FALSE, col.names = TRUE,
showProgress = TRUE)
rm(temp)
AERNASEclass: RangedSummarizedExperiment
dim: 59851 611
metadata(1): ''
assays(1): counts
rownames(59851): ENSG00000000003 ENSG00000000419 ... ENSG00000279119 ENSG00000271242
rowData names(3): feature_id symbol GENEBIOTYPE_EnsDb86
colnames(611): ae1 ae1026 ... ae998 ae999
colData names(58): STUDY_NUMBER SampleType ... OverallPlaquePhenotype Plaque_Vulnerability_Index# saveRDS(AERNASE, file = paste0(OUT_loc, "/", Today, ".AERNA.CEA.608pts.SE.after_qc.IC_commercial.RDS"))
saveRDS(AERNASE, file = paste0(OUT_loc, "/", Today, ".AERNA.CEA.611pts.SE.after_qc.IC_academic.RDS"))Session information
Version: v1.0.0
Last update: 2022-03-17
Written by: Sander W. van der Laan (s.w.vanderlaan-2[at]umcutrecht.nl).
Description: Script to load bulk RNA sequencing data, and perform gene expression analyses, and visualisations.
Minimum requirements: R version 3.5.2 (2018-12-20) -- 'Eggshell Igloo', macOS Mojave (10.14.2).
**MoSCoW To-Do List**
The things we Must, Should, Could, and Would have given the time we have.
_M_
_S_
_C_
_W_
**Changes log**
* v1.0.0 Inital version. Update to the count data, gene list. Filter samples based on artery operated (CEA) and informed consent. Added heatmap of correlation between target genes. sessionInfo()R version 4.1.2 (2021-11-01)
Platform: x86_64-apple-darwin17.0 (64-bit)
Running under: macOS Monterey 12.2.1
Matrix products: default
LAPACK: /Library/Frameworks/R.framework/Versions/4.1/Resources/lib/libRlapack.dylib
locale:
[1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
attached base packages:
[1] stats4 grid tools stats graphics grDevices utils datasets methods base
other attached packages:
[1] labelled_2.9.0 annotables_0.1.91 EnhancedVolcano_1.12.0 ggrepel_0.9.1
[5] EnsDb.Hsapiens.v86_2.99.0 ensembldb_2.18.2 AnnotationFilter_1.18.0 TxDb.Hsapiens.UCSC.hg19.knownGene_3.2.2
[9] mygene_1.30.0 org.Hs.eg.db_3.14.0 DESeq2_1.34.0 SummarizedExperiment_1.24.0
[13] MatrixGenerics_1.6.0 matrixStats_0.61.0 GenomicFeatures_1.46.3 AnnotationDbi_1.56.2
[17] Biobase_2.54.0 GenomicRanges_1.46.1 GenomeInfoDb_1.30.0 IRanges_2.28.0
[21] S4Vectors_0.32.3 BiocGenerics_0.40.0 UpSetR_1.4.0 ggpubr_0.4.0
[25] forestplot_2.0.1 checkmate_2.0.0 magrittr_2.0.1 pheatmap_1.0.12
[29] devtools_2.4.3 usethis_2.1.5 BlandAltmanLeh_0.3.1 sjPlot_2.8.10
[33] tableone_0.13.0 haven_2.4.3 openxlsx_4.2.5 eeptools_1.2.4
[37] DT_0.20 knitr_1.37 forcats_0.5.1 stringr_1.4.0
[41] purrr_0.3.4 tibble_3.1.6 ggplot2_3.3.5 tidyverse_1.3.1
[45] data.table_1.14.2 naniar_0.6.1 tidyr_1.1.4 dplyr_1.0.7
[49] optparse_1.7.1 readr_2.1.1 pander_0.6.4 rmarkdown_2.11
[53] worcs_0.1.9.1
loaded via a namespace (and not attached):
[1] estimability_1.3 rappdirs_0.3.3 rtracklayer_1.54.0 rticles_0.22 coda_0.19-4 ragg_1.2.1
[7] visdat_0.5.3 bit64_4.0.5 multcomp_1.4-18 DelayedArray_0.20.0 rpart_4.1.16 KEGGREST_1.34.0
[13] RCurl_1.98-1.5 generics_0.1.1 callr_3.7.0 TH.data_1.1-0 RSQLite_2.2.9 proxy_0.4-26
[19] chron_2.3-56 bit_4.0.4 tzdb_0.2.0 xml2_1.3.3 lubridate_1.8.0 ggsci_2.9
[25] assertthat_0.2.1 xfun_0.29 hms_1.1.1 evaluate_0.14 fansi_1.0.0 restfulr_0.0.13
[31] progress_1.2.2 dbplyr_2.1.1 readxl_1.3.1 DBI_1.1.2 geneplotter_1.72.0 htmlwidgets_1.5.4
[37] ellipsis_0.3.2 backports_1.4.1 insight_0.16.0 survey_4.1-1 annotate_1.72.0 biomaRt_2.50.2
[43] vctrs_0.3.8 remotes_2.4.2 sjlabelled_1.1.8 abind_1.4-5 cachem_1.0.6 withr_2.4.3
[49] sys_3.4 emmeans_1.7.2 vcd_1.4-9 GenomicAlignments_1.30.0 prettyunits_1.1.1 getopt_1.20.3
[55] cluster_2.1.2 lazyeval_0.2.2 crayon_1.4.2 genefilter_1.76.0 labeling_0.4.2 pkgconfig_2.0.3
[61] vipor_0.4.5 nlme_3.1-155 pkgload_1.2.4 ProtGenerics_1.26.0 nnet_7.3-17 rlang_0.4.12
[67] lifecycle_1.0.1 sandwich_3.0-1 filelock_1.0.2 extrafontdb_1.0 BiocFileCache_2.2.0 modelr_0.1.8
[73] ggrastr_1.0.1 cellranger_1.1.0 rprojroot_2.0.2 lmtest_0.9-39 datawizard_0.3.0 Matrix_1.4-0
[79] carData_3.0-5 boot_1.3-28 zoo_1.8-9 beeswarm_0.4.0 base64enc_0.1-3 reprex_2.0.1
[85] processx_3.5.2 png_0.1-7 rjson_0.2.21 parameters_0.17.0 bitops_1.0-7 KernSmooth_2.23-20
[91] Biostrings_2.62.0 blob_1.2.2 maptools_1.1-2 arm_1.12-2 jpeg_0.1-9 rstatix_0.7.0
[97] ggeffects_1.1.1 ggsignif_0.6.3 scales_1.1.1 memoise_2.0.1 plyr_1.8.6 zlibbioc_1.40.0
[103] compiler_4.1.2 BiocIO_1.4.0 ash_1.0-15 RColorBrewer_1.1-2 lme4_1.1-27.1 Rsamtools_2.10.0
[109] cli_3.1.0 XVector_0.34.0 ps_1.6.0 htmlTable_2.4.0 Formula_1.2-4 MASS_7.3-54
[115] tidyselect_1.1.1 stringi_1.7.6 textshaping_0.3.6 proj4_1.0-10.1 mitools_2.4 yaml_2.2.1
[121] askpass_1.1 locfit_1.5-9.4 latticeExtra_0.6-29 credentials_1.3.2 parallel_4.1.2 rstudioapi_0.13
[127] foreign_0.8-82 gridExtra_2.3 farver_2.1.0 digest_0.6.29 proto_1.0.0 gert_1.5.0
[133] Rcpp_1.0.7 prereg_0.5.0 car_3.0-12 broom_0.7.11 ggalt_0.4.0 performance_0.8.0
[139] httr_1.4.2 effectsize_0.6.0.1 sjstats_0.18.1 colorspace_2.0-2 rvest_1.0.2 XML_3.99-0.8
[145] fs_1.5.2 ranger_0.13.1 splines_4.1.2 sp_1.4-6 systemfonts_1.0.3 sessioninfo_1.2.2
[151] xtable_1.8-4 jsonlite_1.7.2 nloptr_1.2.2.3 testthat_3.1.1 R6_2.5.1 Hmisc_4.6-0
[157] gsubfn_0.7 pillar_1.6.4 htmltools_0.5.2 glue_1.6.0 fastmap_1.1.0 minqa_1.2.4
[163] BiocParallel_1.28.3 class_7.3-20 codetools_0.2-18 maps_3.4.0 pkgbuild_1.3.1 mvtnorm_1.1-3
[169] utf8_1.2.2 lattice_0.20-45 sqldf_0.4-11 ggbeeswarm_0.6.0 curl_4.3.2 Rttf2pt1_1.3.9
[175] zip_2.2.0 openssl_1.4.6 survival_3.3-1 desc_1.4.0 munsell_0.5.0 e1071_1.7-9
[181] GenomeInfoDbData_1.2.7 sjmisc_2.8.9 gtable_0.3.0 extrafont_0.17 bayestestR_0.11.5 Saving environment
save.image(paste0(PROJECT_loc, "/",Today,".",PROJECTNAME,".bulkRNAseq.preparation.RData"))| © 1979-2022 Sander W. van der Laan | s.w.vanderlaan[at]gmail.com swvanderlaan.github.io. |
---
title: "Plaque expression levels of _HDAC9_ in association with plaque vulnerability traits and secondary vascular events in patients undergoing carotid endarterectomy: an analysis in the Athero-EXPRESS Biobank."
author: "[Sander W. van der Laan, PhD](https://swvanderlaan.github.io) | @swvanderlaan | s.w.vanderlaan@gmail.com"
date: "`r Sys.Date()`"
output:
  html_notebook:
    cache: yes
    code_folding: hide
    collapse: yes
    df_print: paged
    fig.align: center
    fig_caption: yes
    fig_height: 6
    fig_retina: 2
    fig_width: 7
    highlight: tango
    theme: lumen
    toc: yes
    toc_float:
      collapsed: no
      smooth_scroll: yes
mainfont: Arial
subtitle: "Athero-Express RNA sequencing preparation"
editor_options:
  chunk_output_type: inline
# bibliography: references.bib
# knit: worcs::cite_all
---

# General Setup

```{r setup, include=FALSE}
# We recommend that you prepare your raw data for analysis in 'prepare_data.R',
# and end that file with either open_data(yourdata), or closed_data(yourdata).
# Then, uncomment the line below to load the original or synthetic data
# (whichever is available), to allow anyone to reproduce your code:
# load_data()

# further define some knitr-options.
knitr::opts_chunk$set(fig.width = 12, fig.height = 8, fig.path = 'Figures/', 
                      warning = TRUE, # show warnings during codebook generation
                      message = TRUE, # show messages during codebook generation
                      error = TRUE, # do not interrupt codebook generation in case of errors, 
                                    # usually better for debugging
                      echo = TRUE,  # show R code
                      eval = TRUE)

ggplot2::theme_set(ggplot2::theme_minimal())
# pander::panderOptions("table.split.table", Inf)
library("worcs")
library("rmarkdown")

```

```{r echo = FALSE}
rm(list = ls())
```

```{r LocalSystem, echo = FALSE}
### Operating System Version
### MacBook Pro
ROOT_loc = "/Users/swvanderlaan"

### MacBook Air 
# ROOT_loc = "/Users/slaan3"

### General
GENOMIC_loc = paste0(ROOT_loc, "/OneDrive - UMC Utrecht/Genomics")
AEDB_loc = paste0(GENOMIC_loc, "/Athero-Express/AE-AAA_GS_DBs")
LAB_loc = paste0(GENOMIC_loc, "/LabBusiness")

PROJECT_loc = paste0(ROOT_loc, "/git/CirculatoryHealth/AE_20211201_YAW_SWVANDERLAAN_HDAC9")

# Genetic and genomic data
STORAGE_loc = paste0(ROOT_loc, "/PLINK")
AERNA_loc = paste0(STORAGE_loc, "/_AE_ORIGINALS/AERNA")
AESCRNA_loc = paste0(STORAGE_loc, "/_AE_ORIGINALS/AESCRNA/prepped_data")
AEGSQC_loc = paste0(STORAGE_loc, "/_AE_ORIGINALS/AEGS_COMBINED_QC2018")

### SOME VARIABLES WE NEED DOWN THE LINE
TRAIT_OF_INTEREST = "HDAC9" # Phenotype
PROJECTNAME = "HDAC9"

cat("\nCreate a new analysis directory...\n")
ifelse(!dir.exists(file.path(PROJECT_loc, "/",PROJECTNAME)), 
       dir.create(file.path(PROJECT_loc, "/",PROJECTNAME)), 
       FALSE)
ANALYSIS_loc = paste0(PROJECT_loc,"/",PROJECTNAME)

ifelse(!dir.exists(file.path(ANALYSIS_loc, "/PLOTS")), 
       dir.create(file.path(ANALYSIS_loc, "/PLOTS")), 
       FALSE)
PLOT_loc = paste0(ANALYSIS_loc,"/PLOTS")

ifelse(!dir.exists(file.path(PLOT_loc, "/QC")), 
       dir.create(file.path(PLOT_loc, "/QC")), 
       FALSE)
QC_loc = paste0(PLOT_loc,"/QC")

ifelse(!dir.exists(file.path(ANALYSIS_loc, "/OUTPUT")), 
       dir.create(file.path(ANALYSIS_loc, "/OUTPUT")), 
       FALSE)
OUT_loc = paste0(ANALYSIS_loc, "/OUTPUT")

ifelse(!dir.exists(file.path(ANALYSIS_loc, "/BASELINE")), 
       dir.create(file.path(ANALYSIS_loc, "/BASELINE")), 
       FALSE)
BASELINE_loc = paste0(ANALYSIS_loc, "/BASELINE")


setwd(paste0(PROJECT_loc))
getwd()
list.files()

```

```{r Source functions}
source(paste0(PROJECT_loc, "/scripts/functions.R"))
```

```{r loading_packages, message=FALSE, warning=FALSE}
install.packages.auto("pander")
install.packages.auto("readr")
install.packages.auto("optparse")
install.packages.auto("tools")
install.packages.auto("dplyr")
install.packages.auto("tidyr")
install.packages.auto("naniar")

# To get 'data.table' with 'fwrite' to be able to directly write gzipped-files
# Ref: https://stackoverflow.com/questions/42788401/is-possible-to-use-fwrite-from-data-table-with-gzfile
# install.packages("data.table", repos = "https://Rdatatable.gitlab.io/data.table")
library(data.table)

install.packages.auto("tidyverse")
install.packages.auto("knitr")
install.packages.auto("DT")
install.packages.auto("eeptools")

install.packages.auto("openxlsx")

install.packages.auto("haven")
install.packages.auto("tableone")
install.packages.auto("sjPlot")

install.packages.auto("BlandAltmanLeh")

# Install the devtools package from Hadley Wickham
install.packages.auto('devtools')

# for plotting
install.packages.auto("pheatmap")
install.packages.auto("forestplot")
install.packages.auto("ggplot2")

install.packages.auto("ggpubr")

install.packages.auto("UpSetR")

devtools::install_github("thomasp85/patchwork")

# for Seurat etc
install.packages.auto("GenomicFeatures")
install.packages.auto("GenomicRanges")
install.packages.auto("SummarizedExperiment")
install.packages.auto("DESeq2")
install.packages.auto("org.Hs.eg.db")
install.packages.auto("mygene")
install.packages.auto("TxDb.Hsapiens.UCSC.hg19.knownGene")
install.packages.auto("org.Hs.eg.db")
install.packages.auto("AnnotationDbi")
install.packages.auto("EnsDb.Hsapiens.v86")
install.packages.auto("EnhancedVolcano")

```

```{r Setting: Colors}

Today = format(as.Date(as.POSIXlt(Sys.time())), "%Y%m%d")
Today.Report = format(as.Date(as.POSIXlt(Sys.time())), "%A, %B %d, %Y")

### UtrechtScienceParkColoursScheme
###
### WebsitetoconvertHEXtoRGB:http://hex.colorrrs.com.
### Forsomefunctionsyoushoulddividethesenumbersby255.
###
###	No.	Color			      HEX	(RGB)						              CHR		  MAF/INFO
###---------------------------------------------------------------------------------------
###	1	  yellow			    #FBB820 (251,184,32)				      =>	1		or 1.0>INFO
###	2	  gold			      #F59D10 (245,157,16)				      =>	2		
###	3	  salmon			    #E55738 (229,87,56)				      =>	3		or 0.05<MAF<0.2 or 0.4<INFO<0.6
###	4	  darkpink		    #DB003F ((219,0,63)				      =>	4		
###	5	  lightpink		    #E35493 (227,84,147)				      =>	5		or 0.8<INFO<1.0
###	6	  pink			      #D5267B (213,38,123)				      =>	6		
###	7	  hardpink		    #CC0071 (204,0,113)				      =>	7		
###	8	  lightpurple	    #A8448A (168,68,138)				      =>	8		
###	9	  purple			    #9A3480 (154,52,128)				      =>	9		
###	10	lavendel		    #8D5B9A (141,91,154)				      =>	10		
###	11	bluepurple		  #705296 (112,82,150)				      =>	11		
###	12	purpleblue		  #686AA9 (104,106,169)			      =>	12		
###	13	lightpurpleblue	#6173AD (97,115,173/101,120,180)	=>	13		
###	14	seablue			    #4C81BF (76,129,191)				      =>	14		
###	15	skyblue			    #2F8BC9 (47,139,201)				      =>	15		
###	16	azurblue		    #1290D9 (18,144,217)				      =>	16		or 0.01<MAF<0.05 or 0.2<INFO<0.4
###	17	lightazurblue	  #1396D8 (19,150,216)				      =>	17		
###	18	greenblue		    #15A6C1 (21,166,193)				      =>	18		
###	19	seaweedgreen	  #5EB17F (94,177,127)				      =>	19		
###	20	yellowgreen		  #86B833 (134,184,51)				      =>	20		
###	21	lightmossgreen	#C5D220 (197,210,32)				      =>	21		
###	22	mossgreen		    #9FC228 (159,194,40)				      =>	22		or MAF>0.20 or 0.6<INFO<0.8
###	23	lightgreen	  	#78B113 (120,177,19)				      =>	23/X
###	24	green			      #49A01D (73,160,29)				      =>	24/Y
###	25	grey			      #595A5C (89,90,92)				        =>	25/XY	or MAF<0.01 or 0.0<INFO<0.2
###	26	lightgrey		    #A2A3A4	(162,163,164)			      =>	26/MT
###
###	ADDITIONAL COLORS
###	27	midgrey			#D7D8D7
###	28	verylightgrey	#ECECEC"
###	29	white			#FFFFFF
###	30	black			#000000
###----------------------------------------------------------------------------------------------

uithof_color = c("#FBB820","#F59D10","#E55738","#DB003F","#E35493","#D5267B",
                 "#CC0071","#A8448A","#9A3480","#8D5B9A","#705296","#686AA9",
                 "#6173AD","#4C81BF","#2F8BC9","#1290D9","#1396D8","#15A6C1",
                 "#5EB17F","#86B833","#C5D220","#9FC228","#78B113","#49A01D",
                 "#595A5C","#A2A3A4", "#D7D8D7", "#ECECEC", "#FFFFFF", "#000000")

uithof_color_legend = c("#FBB820", "#F59D10", "#E55738", "#DB003F", "#E35493",
                        "#D5267B", "#CC0071", "#A8448A", "#9A3480", "#8D5B9A",
                        "#705296", "#686AA9", "#6173AD", "#4C81BF", "#2F8BC9",
                        "#1290D9", "#1396D8", "#15A6C1", "#5EB17F", "#86B833",
                        "#C5D220", "#9FC228", "#78B113", "#49A01D", "#595A5C",
                        "#A2A3A4", "#D7D8D7", "#ECECEC", "#FFFFFF", "#000000")
### ----------------------------------------------------------------------------
```

# ERA-CVD 'druggable-MI-targets'

<!-- ![ERA-CVD logo]("Users/swvanderlaan/iCloud/Genomics/Projects/#Druggable-MI-Genes/Administration/ERA-CVD\ Logo_CMYK.jpg") -->

For the ERA-CVD 'druggable-MI-targets' project (grantnumber: 01KL1802) we performed two related RNA sequencing (RNAseq) experiments:

1)  conventional ('bulk') RNAseq using RNA extracted from carotid plaque samples, n ± 700. As of `r Today.Report` all samples have been selected and
RNA has been extracted; quality control (QC) was performed and we have a dataset of 635 samples.

2)  single-cell RNAseq (scRNAseq) of at least n = 40 samples (20 females, 20 males). As of `r Today.Report` data is available of 40 samples (3 females, 15 males), we are extending sampling to get more female samples.

Plaque samples are derived from carotid endarterectomies as part of the [Athero-Express Biobank Study](http:www/atheroexpress.nl) which is an ongoing study in the UMC Utrecht.

# Background

Here we obtain data from the `r TRAIT_OF_INTEREST` in plaques.

```{r targets, message=FALSE, warning=FALSE}
library(openxlsx)

gene_list_df <- read.xlsx(paste0(PROJECT_loc, "/targets/Genes.xlsx"), sheet = "Genes")

target_genes <- unlist(gene_list_df$Gene)
target_genes

```

# Load data

First we will load the data:

-   bulk RNA sequencing (RNAseq) experimental data from carotid plaques
-   Athero-Express clinical data.

## Bulk RNAseq data

Here we load the latest dataset from our Athero-Express bulk RNA experiment d.d. 2021-12-03 mapped to b37 and Ensembl 87.

These bulk RNAseq data are filtered and corrected:

-   UMI corrected
-   unmappable genes are excluded

```{r LoadData}
# bulk RNAseq data
bulkRNA_counts_raw_qc_umicorr <- fread(paste0(AERNA_loc,"/raw_data_bulk/raw_counts_batch1till11_qc_umicorrected.txt"))

# batch information
bulkRNA_meta <- fread(paste0(AERNA_loc,"/raw_data_bulk/metadata_raw_counts_batch1till11.txt"))

```

Quick peek at the counts and meta-data of the RNAseq experiment.

```{r QuickPeek}

head(bulkRNA_counts_raw_qc_umicorr)

head(bulkRNA_meta)
```

### Annotating and fixing the RNAseq data

There are two small issues we need to address:

-   annotation with chromosome, start/end, strand, and gene information
-   fixing ±`Inf` values

#### Fixing infinite values

```{r}
cat("\nThere are a couple of samples with infinite gene counts.\n")
temp <- bulkRNA_counts_raw_qc_umicorr %>% mutate_if(is.numeric, as.integer)
summary(bulkRNA_counts_raw_qc_umicorr$ae2341)
summary(bulkRNA_counts_raw_qc_umicorr$ae3078)
summary(bulkRNA_counts_raw_qc_umicorr$ae1422)
summary(bulkRNA_counts_raw_qc_umicorr$ae2305)
summary(bulkRNA_counts_raw_qc_umicorr$ae1256)
summary(bulkRNA_counts_raw_qc_umicorr$ae411)
summary(bulkRNA_counts_raw_qc_umicorr$ae1227)

cat("\nFixing the infinite gene counts.\n")
temp <- bulkRNA_counts_raw_qc_umicorr %>%
  mutate(across( # For every column you want...
  starts_with("ae"), # ...change all studynumber
  ~ case_when( 
  . ==  Inf ~ max(.[is.finite(.)]), # +Inf becomes the finite max.
  . == -Inf ~ min(.[is.finite(.)]), # -Inf becomes the finite min.
  TRUE ~ . # Other values stay the same.
  )
  )
  )


```

#### Annotating

```{r}

library("devtools")
devtools::install_github("stephenturner/annotables")
library(dplyr)
library(annotables)

# Columns of interest
# entrez
# symbol
# chr
# start
# end
# strand
# biotype
# description

cat("\nChecking existence of duplicate ENSEMBL IDs - there shouldn't be any.\n")
id <- temp$ENSEMBL_gene_ID
id[ id %in% id[duplicated(id)] ]
rm(id)
```

```{r}
cat("\nAnnotating with b37.\n")
bulkRNA_counts <- temp %>% 
  # arrange(p.adjusted) %>% 
  # head(20) %>% 
  inner_join(grch37, by=c("ENSEMBL_gene_ID"="ensgene")) %>%
  # select(gene, estimate, p.adjusted, symbol, description) %>% 
  relocate(entrez, symbol, chr, start, end, strand, biotype, description, 
           .before = ae1618) %>%
  dplyr::filter(duplicated(ENSEMBL_gene_ID) == FALSE)
head(bulkRNA_counts)


id <- bulkRNA_counts$ENSEMBL_gene_ID
id[ id %in% id[duplicated(id)] ]

```

## Clinical data

Loading Athero-Express clinical data that we previously saved in an RDS file.

```{r LoadAEDB}
AEDB.CEA <- readRDS(file = paste0(OUT_loc, "/20220317.HDAC9.AEDB.CEA.RDS"))

```

### Fix STUDY_NUMBER

We will fix the `STUDY_NUMBER` to match the bulkRNAseq data.

```{r FixStudyNumber}

AEDB.CEA$STUDY_NUMBER <- paste0("ae", AEDB.CEA$STUDY_NUMBER)
head(AEDB.CEA$STUDY_NUMBER)

```


# AERNA

## Tidy data

We have collected the clinical data, Athero-Express Biobank Study `AEDB` and, the UMI-corrected, filtered bulk RNAseq data, `bulkRNA_counts` and its meta-data, `bulkRNA-meta`.

Here we will clean up the data and create a `SummarizedExperiment()` object for downstream analyses anad visualizations.

```{r Parsing RNAseq, message=FALSE, warning=FALSE}
AEDB.CEA.sampleList <- AEDB.CEA$STUDY_NUMBER

# first 9 columns
# ENSEMBL_gene_ID
# entrez
# symbol
# chr
# start
# end
# strand
# biotype
# description

# match up with meta data of RNAseq experiment
bulkRNA_countsFilt <- bulkRNA_counts %>%
  drop_na(chr) %>%   # remove rows that have no information of start, end, chromosome and/or strand
  dplyr::select(1:9, one_of(sort(as.character(AEDB.CEA.sampleList)))) # select gene expression of only patients in RNA-seq AE df, sort in same order as metadata study_number
dim(bulkRNA_countsFilt)

study_samples_bulkNEW <- colnames(bulkRNA_counts[, -(1:9)])
length(study_samples_bulkNEW)
study_samples_AEDBCEA <- c(AEDB.CEA$STUDY_NUMBER)

setdif_samples_NEWvsAEDBCEA <- setdiff(study_samples_bulkNEW, study_samples_AEDBCEA)
setdif_samples_AEDBCEAvsNEW <- setdiff(study_samples_AEDBCEA, study_samples_bulkNEW)

AEDB_filt <- AEDB.CEA[AEDB.CEA$STUDY_NUMBER %in% setdif_samples_NEWvsAEDBCEA,]
table(AEDB_filt$Artery_summary, AEDB_filt$Gender)

# Cut up bulkRNA_countsFilt into 'assay' and 'ranges' part
counts <- as.data.frame(bulkRNA_countsFilt[,-(1:9)])  ## assay part
counts <- counts %>% mutate_if(is.numeric, as.integer)

rownames(counts) <- bulkRNA_countsFilt$ENSEMBL_gene_ID  ## assign rownames

id <- bulkRNA_countsFilt$ENSEMBL_gene_ID
id[ id %in% id[duplicated(id)] ]

bulkRNA_rowRanges <- GRanges(bulkRNA_countsFilt$chr,	 ## construct a GRanges object containing 4 columns (seqnames, ranges, strand, seqinfo) plus a metadata colum (feature_id): this will be the 'rowRanges' bit
                     IRanges(bulkRNA_countsFilt$start, bulkRNA_countsFilt$end),
                     strand = bulkRNA_countsFilt$strand,
                     feature_id = bulkRNA_countsFilt$ENSEMBL_gene_ID) #, df$pid)
names(bulkRNA_rowRanges) <- bulkRNA_rowRanges$feature_id

# ?org.Hs.eg.db
# ?AnnotationDb

bulkRNA_rowRanges$symbol <- mapIds(org.Hs.eg.db,
                     keys = bulkRNA_rowRanges$feature_id,
                     column = "SYMBOL",
                     keytype = "ENSEMBL",
                     multiVals = "first")

# Reference: https://shiring.github.io/genome/2016/10/23/AnnotationDbi

# gene dataframe for EnsDb.Hsapiens.v86
gene_dataframe_EnsDb <- ensembldb::select(EnsDb.Hsapiens.v86, keys = bulkRNA_rowRanges$feature_id,
                                          columns = c("ENTREZID", "SYMBOL", "GENEBIOTYPE"), keytype = "GENEID")
colnames(gene_dataframe_EnsDb) <- c("Ensembl", "Entrez", "HGNC", "GENEBIOTYPE")
colnames(gene_dataframe_EnsDb) <- paste(colnames(gene_dataframe_EnsDb), "EnsDb86", sep = "_")
head(gene_dataframe_EnsDb)


bulkRNA_rowRanges$GENEBIOTYPE_EnsDb86 <- gene_dataframe_EnsDb$GENEBIOTYPE_EnsDb86[match(bulkRNA_rowRanges$feature_id, gene_dataframe_EnsDb$Ensembl_EnsDb86)]
bulkRNA_rowRanges

# merging the two dataframes by HGNC
# bulkRNA_rowRangesHg19Ensemblb86 <- GRanges(merge(bulkRNA_rowRanges, gene_dataframe_EnsDb, by.x = "feature_id", by.y = "Ensembl_EnsDb86", sort = FALSE, all.x = TRUE))
# names(bulkRNA_rowRangesHg19Ensemblb86) <- bulkRNA_rowRangesHg19Ensemblb86$feature_id
# bulkRNA_rowRangesHg19Ensemblb86

# temp <- as.data.frame(table(bulkRNA_rowRanges$GENEBIOTYPE_EnsDb86))
# colnames(temp) <- c("GeneBiotype", "Count")
# 
# ggpubr::ggbarplot(temp, x = "GeneBiotype", y = "Count",
#                   color = "GeneBiotype", fill = "GeneBiotype",
#                   xlab = "gene type") + 
#   theme(axis.text.x = element_text(angle = 45))
# rm(temp)

```

```{r Parse ClinicalData RNAseq}
# match up with meta data of RNAseq experiment
bulkRNA_meta %<>%
     dplyr::filter(study_number %in% AEDB.CEA.sampleList) # select gene expression of only patients in RNA-seq AE df, sort in same order as metadata study_number

# combine meta data from experiment with clinical data
bulkRNA_meta_clin <- merge(bulkRNA_meta, AEDB.CEA, by.x = "study_number", by.y = "STUDY_NUMBER",
                           sort = FALSE, all.x = TRUE)

bulkRNA_meta_clin %<>%
  # mutate(macrophages = factor(macrophages, levels = c("no staining", "minor staining", "moderate staining", "heavy staining"))) %>% 
  # mutate(smc = factor(smc, levels = c("no staining", "minor staining", "moderate staining", "heavy staining"))) %>% 
  # mutate(calcification = factor(calcification, levels = c("no staining", "minor staining", "moderate staining", "heavy staining"))) %>% 
  # mutate(collagen = factor(collagen, levels = c("no staining", "minor staining", "moderate staining", "heavy staining"))) %>% 
  # mutate(fat = factor(fat, levels = c("no fat", "< 40% fat", "> 40% fat"))) %>% 
  mutate(study_number_row = study_number) %>%
  as.data.frame() %>%
  column_to_rownames("study_number_row")

head(bulkRNA_meta_clin)
dim(bulkRNA_meta_clin)

```

## SummarizedExperiment

We make a `SummarizedExperiment` for the RNAseq data. We make sure to only include the samples we need based on informed consent and we include only the requested variables.

First, we define the variables we need.

```{r}

# Baseline table variables
basetable_vars = c("Hospital", "ORyear", "Artery_summary",
                   "Age", "Gender", 
                   # "TC_finalCU", "LDL_finalCU", "HDL_finalCU", "TG_finalCU", 
                   "TC_final", "LDL_final", "HDL_final", "TG_final", 
                   # "hsCRP_plasma",
                   "systolic", "diastoli", "GFR_MDRD", "BMI", 
                   "KDOQI", "BMI_WHO",
                   "SmokerStatus", "AlcoholUse",
                   "DiabetesStatus", 
                   "Hypertension.selfreport", "Hypertension.selfreportdrug", "Hypertension.composite", "Hypertension.drugs", 
                   "Med.anticoagulants", "Med.all.antiplatelet", "Med.Statin.LLD", 
                   "Stroke_Dx", "sympt", "Symptoms.5G", "AsymptSympt", "AsymptSympt2G",
                   "Symptoms.Update2G", "Symptoms.Update3G",
                   "restenos", "stenose",
                   "CAD_history", "PAOD", "Peripheral.interv", 
                   "EP_composite", "EP_composite_time", "EP_major", "EP_major_time",
                   "MAC_rankNorm", "SMC_rankNorm", "Macrophages.bin", "SMC.bin",
                   "Neutrophils_rankNorm", "MastCells_rankNorm",
                   "IPH.bin", "VesselDensity_rankNorm",
                   "Calc.bin", "Collagen.bin", 
                   "Fat.bin_10", "Fat.bin_40", 
                   "OverallPlaquePhenotype", "Plaque_Vulnerability_Index")

basetable_bin = c("Gender", "Artery_summary",
                  "KDOQI", "BMI_WHO",
                  "SmokerStatus", "AlcoholUse",
                  "DiabetesStatus", 
                  "Hypertension.selfreport", "Hypertension.selfreportdrug", "Hypertension.composite", "Hypertension.drugs", 
                  "Med.anticoagulants", "Med.all.antiplatelet", "Med.Statin.LLD", 
                  "Stroke_Dx", "sympt", "Symptoms.5G", "AsymptSympt", "AsymptSympt2G",
                  "Symptoms.Update2G", "Symptoms.Update3G",
                  "restenos", "stenose",
                  "CAD_history", "PAOD", "Peripheral.interv", 
                  "EP_composite", "Macrophages.bin", "SMC.bin",
                  "IPH.bin", 
                  "Calc.bin", "Collagen.bin", 
                  "Fat.bin_10", "Fat.bin_40", 
                  "OverallPlaquePhenotype", "Plaque_Vulnerability_Index")
# basetable_bin

basetable_con = basetable_vars[!basetable_vars %in% basetable_bin]
# basetable_con

```


Next, we are constructing the `SummarizedExperiment`.
```{r RNAseq to SE}
cat("* loading data ...\n")

# this is all the data passing RNAseq quality control and UMI-corrected
# - includes 656 patients
# - after filtering on informed consent and artery type, the end sample size should be 611
# - after filtering on 'no commercial business' based on informed consent, there are fewer samples: 608
dim(bulkRNA_countsFilt)
dim(counts)
cat("\n* making a SummarizedExperiment ...\n")
cat("  > getting counts\n")
head(counts)
head(bulkRNA_countsFilt)

cat("  > meta data\n")
temp_coldat <- data.frame(STUDY_NUMBER = names(bulkRNA_countsFilt[,10:620]), 
                          SampleType = "plaque", RNAseqType = "3' RNAseq", 
                          row.names = names(bulkRNA_countsFilt[,10:620]))
cat("  > clinical data\n")
# bulkRNA_meta_clin_COMMERCIAL <- subset(bulkRNA_meta_clin, select = c("study_number", basetable_vars))
bulkRNA_meta_clin_ACADEMIC <- subset(bulkRNA_meta_clin, select = c("study_number", basetable_vars))

# temp_coldat_clin <- merge(temp_coldat, bulkRNA_meta_clin_COMMERCIAL, by.x = "STUDY_NUMBER", by.y = "study_number", sort = FALSE, all.x = TRUE)
temp_coldat_clin <- merge(temp_coldat, bulkRNA_meta_clin_ACADEMIC, by.x = "STUDY_NUMBER", by.y = "study_number", sort = FALSE, all.x = TRUE)

rownames(temp_coldat_clin) <- temp_coldat_clin$STUDY_NUMBER
dim(temp_coldat_clin)

cat("  > construction of the SE\n")
(AERNASE <- SummarizedExperiment(assays = list(counts = as.matrix(counts)),
                                colData = temp_coldat_clin, 
                                rowRanges = bulkRNA_rowRanges,
                                metadata = "Athero-Express Biobank Study bulk RNA sequencing. Sample type: carotid plaques. Technology: CEL2-seq adapted for bulk RNA sequencing, thus 3'-focused. UMI-corrected"))

cat("\n* removing intermediate files ...\n")
rm(temp_coldat, temp_coldat_clin)

```

Do the study numbers correspond between metadata and expression data?

```{r matching_names}
## check whether rownames metadata and colnames counts are identical
all(colnames(AERNASE) == colnames(counts))

```

So, now we have raw counts for all patients included in the bulk RNAseq data,
with all clinical data annotated to them.

Some of the patients might be missing in certain variables:

```{r missing_values, eval = FALSE}
# We know that some of the patients of the RNAseq is not included in some variables
which(is.na(AERNASE$Gender)) 

missing_values <- which(is.na(AERNASE$Gender))
missing_values
```

No need to remove missing samples based on a variable, since we will make a
DESeq2 object using an empty model.

```{r remove_missing, eval = FALSE}
(AERNASE <- AERNASE[,])
# (AERNASE <- AERNASE[, -missing_values])
# (se <- se[, se$sex == "male"])


```

## Baseline

### Preparation

```{r }
cat("====================================================================================================")
cat("SELECTION THE SHIZZLE")
AERNASEClinData <- as.tibble(colData(AERNASE))

cat("- sanity checking PRIOR to selection")
library(data.table)
require(labelled)
ae.gender <- to_factor(AERNASEClinData$Gender)
ae.hospital <- to_factor(AERNASEClinData$Hospital)
table(ae.gender, ae.hospital, dnn = c("Sex", "Hospital"), useNA = "ifany")

ae.artery <- to_factor(AERNASEClinData$Artery_summary)
table(ae.artery, ae.gender, dnn = c("Sex", "Artery"), useNA = "ifany")

rm(ae.gender, ae.hospital, ae.artery)

# AERNASEClinData[1:10, 1:10]
dim(AERNASEClinData)
# DT::datatable(AERNASEClinData)

```


Showing the baseline table for the scRNAseq data in 39 CEA patients with
informed consent.

```{r Baseline: Visualize}
cat("===========================================================================================")
cat("CREATE BASELINE TABLE")

# Create baseline tables
# http://rstudio-pubs-static.s3.amazonaws.com/13321_da314633db924dc78986a850813a50d5.html
AERNASEClinData.CEA.tableOne = print(CreateTableOne(vars = basetable_vars, 
                                                  # factorVars = basetable_bin,
                                                  # strata = "Gender",
                                                  data = AERNASEClinData, includeNA = TRUE), 
                                   nonnormal = c(), 
                                   quote = FALSE, showAllLevels = TRUE,
                                   format = "p", 
                                   contDigits = 3)[,1:2]

```

Writing the baseline table to Excel format.

```{r }
# Write basetable
require(openxlsx)
# write.xlsx(file = paste0(BASELINE_loc, "/",Today,".",PROJECTNAME,".AERNA.CEA.608pts.after_qc.IC_commercial.BaselineTable..xlsx"), 
#            format(AERNASEClinData.CEA.tableOne, digits = 5, scientific = FALSE) , 
#            rowNames = TRUE, colNames = TRUE, overwrite = TRUE)
# 
write.xlsx(file = paste0(BASELINE_loc, "/",Today,".",PROJECTNAME,".AERNA.CEA.611pts.after_qc.IC_academic.BaselineTable..xlsx"), 
           format(AERNASEClinData.CEA.tableOne, digits = 5, scientific = FALSE) , 
           rowNames = TRUE, colNames = TRUE, overwrite = TRUE)

```



# Expression differences

From here we can analyze whether specific genes differ between groups, or do this for the entire gene set as part of DE analysis, and then select our genes of interest. Let's start with the former.

## Prepare DDS and VSD

The dds raw counts need normalization and log transformation first.

```{r model_exploration, cache = TRUE}
AERNAdds <- DESeqDataSet(AERNASE, design = ~ 1)

# Determine the size factors to use for normalization
AERNAdds <- estimateSizeFactors(AERNAdds)

# sizeFactors(AERNAdds)

# Extract the normalized counts
normalized_counts <- counts(AERNAdds, normalized = TRUE)
# head(normalized_counts)

# Log transform counts for QC
AERNAvsd <- vst(AERNAdds, blind = TRUE)

# There is a message stating the following.
# 
# -- note: fitType='parametric', but the dispersion trend was not well captured by the
#    function: y = a/x + b, and a local regression fit was automatically substituted.
#    specify fitType='local' or 'mean' to avoid this message next time.
#    
# No action is required. 
# 
# For more information check: https://www.biostars.org/p/119115/

```

## Extract data of interest

From here, extract the gene expression values, plus gene identifier, annotate with gene symbol, and select the genes of our interest.

```{r expression_data_selection}
expression_data <- assay(AERNAvsd)

# extract expression values from vsd, including ensembl names
expression_data <- as_tibble(data.frame(gene_ensembl = rowRanges(AERNAvsd)$feature_id, assay(AERNAvsd))) %>%
     mutate_at(vars(c("gene_ensembl")), list(as.character)) ## gene_ensembl needs to be character for annotation to work

# annotations
# gene symbol - via org.Hs.eg.db
# columns(org.Hs.eg.db)
expression_data$symbol <- mapIds(org.Hs.eg.db,
                    keys = expression_data$gene_ensembl,
                    column = "SYMBOL",
                    keytype = "ENSEMBL",
                    multiVals = "first")

# tidy and subset
expression_data_sel <- expression_data %>%
     dplyr::select(gene_ensembl, symbol, everything()) %>%
     # filter(symbol == "APOE" | symbol == "TRIB3") %>% # filter APOE and TRIB3
     dplyr::filter(symbol %in% target_genes)

head(expression_data_sel)

# tidy and subset non-selected genes
set.seed(141619)
expression_data_sample <- expression_data %>%
     dplyr::select(gene_ensembl, symbol, everything()) %>%
     sample_n(1000) %>%
     unite(symbol_ensembl, symbol, gene_ensembl, sep = "_", remove = FALSE)

expression_data_sample_mean <- expression_data_sample %>%
  select_if(is.numeric) %>%
  colMeans() %>%
  as_tibble(rownames = "study_number") %>%
  dplyr::rename(expression_value_sample = value)

```

Furthermore, the expression_data_sel df was gathered into a long form `df` for annotation with symptoms variables from the `vsd` object, and later visualization and statistics.

```{r gather}
# gather expression_data_sel df into long df form for annotation, plotting and statistics
expression_long <-
     gather(expression_data_sel, key = "study_number", value = "expression_value", -c(gene_ensembl, symbol))

# old school way
# Annotate with smoking variables
# sample_ids <- expression_long$study_number
# mm <- match(expression_long$study_number, rownames(colData(vsd)))
#
# ## Add traits to df
# ## Binary traits
# expression_long$sex <- colData(vsd)$sex[mm]
# expression_long$testosterone <- colData(vsd)$testosterone[mm]
# expression_long$t_e2_ratio <- colData(vsd)$t_e2_ratio[mm]

# new school way
plaque_phenotypes_cat <- c("Macrophages.bin",
                           "SMC.bin",
                           "Calc.bin",
                           "Collagen.bin",
                           "Fat.bin_10", 
                           # "Fat.bin_40",
                           "IPH.bin")

plaque_phenotypes_num <- c("MAC_rankNorm", #"macmean0",
                           "SMC_rankNorm", #"smcmean0",
                           # "MastCells_rankNorm", #"mast_cells_plaque",
                           # "Neutrophils_rankNorm", #"neutrophils",
                           "VesselDensity_rankNorm") #"vessel_density")

expression_long <- expression_long %>%
  left_join(bulkRNA_meta_clin %>% dplyr::select(study_number,
                                       plaque_phenotypes_cat,
                                       plaque_phenotypes_num,
                                       epmajor.3years, epmajor.30days,
                                       AsymptSympt2G,
                                       Gender, Hospital),
            by = "study_number") %>%
  mutate(epmajor_3years_yn = str_replace_all(epmajor.3years, c("Excluded" = "yes", "Included" = "no"))) %>%
  mutate(epmajor.30days_yn = str_replace_all(epmajor.30days, c("Excluded" = "yes", "Included" = "no")))

head(expression_long)

# expression_long %>%
#   write_tsv("genes_interest_expression.txt")

```

## Gene expression - distribution

### Filter genes

In case some genes are not available in our data we could filter them here.

```{r list target genes}
target_genes
```

This code is just an example to filter the list from genes that are not in the data.

```{r filter target genes}
# target_genes_rm <- c("AC011294.3", "C6orf195", "C9orf53", "AL137026.1", "DUPD1", "RP11-145E5.5", "PVRL2",
#                      "LINC00841", "LOC100130539")
# 
# temp = target_genes[!target_genes %in% target_genes_rm]
# 
# target_genes_qc <- c(temp)

target_genes_qc <- target_genes

# for debug
# target_genes_qc_replace <- c("LINC01600", "DUSP27", "NECTIN2", "C10orf142", "LINC02881")


```

### Plotting expression

**Figure 1: Expression of genes of interest: boxplots**

```{r boxplots_expression}
# Make directory for plots
ifelse(!dir.exists(file.path(QC_loc, "/Boxplots")), 
       dir.create(file.path(QC_loc, "/Boxplots")), 
       FALSE)
BOX_loc = paste0(QC_loc,"/Boxplots")

for(GENE in target_genes_qc){
  cat(paste0("Plotting expression for ", GENE,".\n"))
  temp <- subset(expression_long, symbol == GENE)
  
  compare_means(expression_value ~ Gender, data = temp)
  
  p1 <- ggpubr::ggboxplot(temp,
                          x = "Gender",
                          y = "expression_value",
                          color = "Gender",
                          palette = "npg",
                          add = "jitter",
                          ylab = paste0("normalized expression ", GENE,"" ),
                          repel = TRUE
                          ) + stat_compare_means()
  #print(p1)
  cat(paste0("Saving image for ", GENE,".\n"))
  
  ggsave(filename = paste0(BOX_loc, "/", Today, ".",GENE,".expression_vs_gender.png"), plot = last_plot())
  ggsave(filename = paste0(BOX_loc, "/", Today, ".",GENE,".expression_vs_gender.pdf"), plot = last_plot())
  
  rm(temp, p1 )
}

```

**Figure 2A: Expression of genes of interest: histograms**

```{r hist_expression, message=FALSE, warning=FALSE}
# Make directory for plots
ifelse(!dir.exists(file.path(QC_loc, "/Histograms")), 
       dir.create(file.path(QC_loc, "/Histograms")), 
       FALSE)
HISTOGRAM_loc = paste0(QC_loc,"/Histograms")

for(GENE in target_genes_qc){
  # cat(paste0("Plotting expression for ", GENE,".\n"))
  temp <- subset(expression_long, symbol == GENE)
  p1 <- ggpubr::gghistogram(temp,
                          x = "expression_value",
                          y = "..count..",
                          color = "Gender", fill = "Gender",
                          palette = "npg",
                          add = "median",
                          ylab = paste0("normalized expression ", GENE,"" )  
                          )
  # print(p1)
  cat(paste0("Saving image for ", GENE,".\n"))
  ggsave(filename = paste0(HISTOGRAM_loc, "/", Today, ".",GENE,".distribution.png"), plot = last_plot())
  # ggsave(filename = paste0(HISTOGRAM_loc, "/", Today, ".",GENE,".distribution.pdf"), plot = last_plot())

  rm(temp, p1 )
}

```

**Figure 2B: Expression of genes of interest: density plots**

```{r dens_expression, message=FALSE, warning=FALSE}
# Make directory for plots
ifelse(!dir.exists(file.path(QC_loc, "/Density")), 
       dir.create(file.path(QC_loc, "/Density")), 
       FALSE)
DENSITY_loc = paste0(QC_loc,"/Density")

for(GENE in target_genes_qc){
  # cat(paste0("Plotting expression for ", GENE,".\n"))
  temp <- subset(expression_long, symbol == GENE)
  p1 <- ggpubr::gghistogram(temp,
                          x = "expression_value",
                          y = "..density..",
                          color = "Gender", fill = "Gender",
                          palette = "npg",
                          add = "median",
                          ylab = paste0("normalized expression ", GENE,"" )  
                          )
  # print(p1)
  cat(paste0("Saving image for ", GENE,".\n"))
  ggsave(filename = paste0(DENSITY_loc, "/", Today, ".",GENE,".density.png"), plot = last_plot())
  # ggsave(filename = paste0(DENSITY_loc, "/", Today, ".",GENE,".density.pdf"), plot = last_plot())
  
  rm(temp, p1 )
}

```

## Compare expression to the expression of a sample of 1,000 genes

**Figure 3: comparing expression of genes of interest to mean expression of a sample of 1,000 random genes**

```{r boxplots_expression_comparison, message=FALSE, warning=FALSE}

expression_wide <- expression_long %>%
  dplyr::select(-gene_ensembl) %>%
  spread(key = symbol, value = expression_value)

```

```{r }
# the next 3 lines of code gave an error when selecting for genes_interest, since one of the genes of interest is missing: FGF3 is not in the data set. So, we need to select for the other 15 genes.
# genes_interest <- genes_interest[genes_interest$Symbol %in% unique(expression_long$symbol),]
# target_genes_qc

expression_wide2 <- expression_wide %>%
  left_join(expression_data_sample_mean, by = "study_number") %>%
  dplyr::select(study_number, target_genes_qc, expression_value_sample)

expression_long2 <- expression_wide2 %>%
  gather(gene, expression_value, -study_number) %>%
  mutate(gene = str_replace_all(gene, c("expression_value_sample" = "Random genes"))) #%>%
  # mutate(gene = factor(gene, levels = c("Random genes", target_genes_qc)))

mean_1000_genes <- mean(expression_data_sample_mean$expression_value_sample)
# head(expression_long2)
# 

  p1 <- ggpubr::ggboxplot(expression_long2,
                          x = "gene",
                          y = "expression_value",
                          color = uithof_color[16],
                          add = "jitter",
                          add.params = list(size = 3, jitter = 0.3), 
                          ylab = paste0("normalized expression ")
                          ) +
    geom_hline(yintercept = mean_1000_genes, linetype = "dashed", color = uithof_color[26], size = 1.25) + 
    theme(axis.text.x = element_text(size = 18, angle = 45, hjust = 1, vjust = 1), # change orientation of x-axis labels
          axis.text.y = element_text(size = 18),
          axis.title.x = element_text(size = 20),
          axis.title.y = element_text(size = 20)) 
  p1
  
  ggsave(filename = paste0(PLOT_loc, "/", Today, ".TargetExpression_vs_1000genes.png"), plot = last_plot())
  ggsave(filename = paste0(PLOT_loc, "/", Today, ".TargetExpression_vs_1000genes.pdf"), plot = last_plot())
  
  rm(p1 )


```

## Heatmaps for genes of interest

If we would put these correlations in one simple and comprehensible figure, we could use a correlation heatmap. Again, correlation coefficients used here are Spearman's.

**Figure 4: correlation heatmap between expression of genes of interest**

```{r heatmap_corr_genes, message=FALSE, warning=FALSE}
library(tidyverse)
library(magrittr)

temp <- expression_wide %>%
  column_to_rownames("study_number") %>%
  dplyr::select(target_genes_qc) %>% 
  drop_na() %>% # drop NA 
  Filter(function(x) sd(x) != 0, .) # filter variables with sd = 0

temp.cor <- cor(temp, method = "spearman") 

p1 <- pheatmap(data.matrix(temp.cor), 
               scale = "none",
               cluster_rows = TRUE, 
               cluster_cols = TRUE,
               legend = TRUE,
               fontsize = 18)
p1

ggsave(filename = paste0(PLOT_loc, "/", Today, ".correlations.target_genes.pdf"), plot = p1, height = 15, width = 15)

ggsave(filename = paste0(PLOT_loc, "/", Today, ".correlations.target_genes.png"), plot = p1, height = 15, width = 15)


rm(temp, temp.cor, p1)

```

# Gene target list

We are saving the final list of genes of interest

```{r Save target genes}

temp <- subset(expression_data_sel, select = c("gene_ensembl", "symbol"))

fwrite(temp,
       file = paste0(OUT_loc, "/", Today, ".target_list.qc.txt"),
       sep = " ", row.names = FALSE, col.names = TRUE,
       showProgress = TRUE)
rm(temp)

```

# Saving AERNA data

We will create a list of samples that should be included based on CEA, and having the proper informed consent ('academic'). We will save the `SummarizedExperiment` as a RDS file for easy loading. The clinical data will also be saved as a separate `txt`-file.

```{r}

temp <- as.tibble(subset(colData(AERNASE), select = c("STUDY_NUMBER", "SampleType", "RNAseqType")))

# fwrite(temp,
#        file = paste0(OUT_loc, "/", Today, ".AERNA.CEA.608pts.samplelist.after_qc.IC_commercial.csv"),
#        sep = ",", row.names = FALSE, col.names = TRUE,
#        showProgress = TRUE)
# rm(temp)
# 
# temp <- as.tibble(colData(AERNASE))
# 
# fwrite(temp,
#        file = paste0(OUT_loc, "/", Today, ".AERNA.CEA.608pts.clinicaldata.after_qc.IC_commercial.csv"),
#        sep = ",", row.names = FALSE, col.names = TRUE,
#        showProgress = TRUE)
# rm(temp)

fwrite(temp,
       file = paste0(OUT_loc, "/", Today, ".AERNA.CEA.611pts.samplelist.after_qc.IC_academic.csv"),
       sep = ",", row.names = FALSE, col.names = TRUE,
       showProgress = TRUE)
rm(temp)

temp <- as.tibble(colData(AERNASE))

fwrite(temp,
       file = paste0(OUT_loc, "/", Today, ".AERNA.CEA.611pts.clinicaldata.after_qc.IC_academic.csv"),
       sep = ",", row.names = FALSE, col.names = TRUE,
       showProgress = TRUE)
rm(temp)

```


```{r}

AERNASE

# saveRDS(AERNASE, file = paste0(OUT_loc, "/", Today, ".AERNA.CEA.608pts.SE.after_qc.IC_commercial.RDS"))
saveRDS(AERNASE, file = paste0(OUT_loc, "/", Today, ".AERNA.CEA.611pts.SE.after_qc.IC_academic.RDS"))

```


# Session information

--------------------------------------------------------------------------------

    Version:      v1.0.0
    Last update:  2022-03-17
    Written by:   Sander W. van der Laan (s.w.vanderlaan-2[at]umcutrecht.nl).
    Description:  Script to load bulk RNA sequencing data, and perform gene expression analyses, and visualisations.
    Minimum requirements: R version 3.5.2 (2018-12-20) -- 'Eggshell Igloo', macOS Mojave (10.14.2).

    **MoSCoW To-Do List**
    The things we Must, Should, Could, and Would have given the time we have.
    _M_

    _S_

    _C_

    _W_

    **Changes log**
    * v1.0.0 Inital version. Update to the count data, gene list. Filter samples based on artery operated (CEA) and informed consent. Added heatmap of correlation between target genes. 

--------------------------------------------------------------------------------

```{r eval = TRUE}
sessionInfo()
```

# Saving environment

```{r Saving}
save.image(paste0(PROJECT_loc, "/",Today,".",PROJECTNAME,".bulkRNAseq.preparation.RData"))
```

+---------------------------------------------------------------------------------------------------------------------------------------+
| <sup>© 1979-2022 Sander W. van der Laan | s.w.vanderlaan[at]gmail.com [swvanderlaan.github.io](https://swvanderlaan.github.io).</sup> |
+---------------------------------------------------------------------------------------------------------------------------------------+
